http://www.lptms.universite-paris-saclay.fr//wiki-cours/api.php?action=feedcontributions&feedformat=atom&user=Michel.ferrero 2026-05-01T22:50:57Z User contributions MediaWiki 1.43.6 http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1857 2022-10-13T10:29:59Z

<p>Michel.ferrero: </p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> <br /> == Breaking news ==<br /> <br /> * You can find the solutions of the MCQ [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers are marked in bold.<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * Solutions: You can find them [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers are marked in bold.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1856 2022-10-13T10:29:46Z

<p>Michel.ferrero: </p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> <br /> == Breaking news ==<br /> <br /> * You can find the solutions of the MCQ [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers<br /> are marked in bold.<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * Solutions: You can find them [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers are marked in bold.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1855 2022-10-13T10:28:02Z

<p>Michel.ferrero: </p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> <br /> == Breaking news ==<br /> <br /> * You can find the solutions of the MCQ [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * Solutions: You can find them [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here].<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1854 2022-10-13T10:26:05Z

<p>Michel.ferrero: </p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> <br /> == Breaking news ==<br /> <br /> * You can find the solutions of the MCQ [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1851 2022-10-07T15:44:21Z

<p>Michel.ferrero: /* Breaking news */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> &lt;!--<br /> == Breaking news ==<br /> --&gt;<br /> <br /> &lt;!-- * Exam: The MCQ is [https://drive.google.com/file/d/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv/view?usp=sharing here] <br /> --&gt;<br /> <br /> &lt;!--<br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions can be found [https://drive.google.com/file/d/1VkPOa2rc0npZf5nMMfaTxf_ou4ObRMSv/view?usp=sharing here] (correct answers are put in bold).<br /> --&gt;<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1850 2022-10-07T12:24:18Z

<p>Michel.ferrero: /* Breaking news */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Breaking news ==<br /> <br /> * Good luck!<br /> <br /> &lt;!-- * Exam: The MCQ is [https://drive.google.com/file/d/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv/view?usp=sharing here] <br /> --&gt;<br /> <br /> &lt;!--<br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions can be found [https://drive.google.com/file/d/1VkPOa2rc0npZf5nMMfaTxf_ou4ObRMSv/view?usp=sharing here] (correct answers are put in bold).<br /> --&gt;<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1845 2022-10-07T12:05:36Z

<p>Michel.ferrero: /* Breaking news */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Breaking news ==<br /> <br /> * Exam: The MCQ is [https://colab.research.google.com/drive/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv?authuser=1 here]<br /> <br /> &lt;!--<br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions can be found [https://drive.google.com/file/d/1VkPOa2rc0npZf5nMMfaTxf_ou4ObRMSv/view?usp=sharing here] (correct answers are put in bold).<br /> --&gt;<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1844 2022-10-07T11:56:06Z

<p>Michel.ferrero: /* Breaking news */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Breaking news ==<br /> <br /> &lt;!--<br /> * Exam: The MCQ is [https://colab.research.google.com/drive/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv?authuser=1 here]<br /> --&gt;<br /> <br /> &lt;!--<br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions can be found [https://drive.google.com/file/d/1VkPOa2rc0npZf5nMMfaTxf_ou4ObRMSv/view?usp=sharing here] (correct answers are put in bold).<br /> --&gt;<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1842 2022-10-03T15:11:11Z

<p>Michel.ferrero: /* Schedule Data-driven Physics */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Breaking news ==<br /> <br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions can be found [https://drive.google.com/file/d/1VkPOa2rc0npZf5nMMfaTxf_ou4ObRMSv/view?usp=sharing here] (correct answers are put in bold).<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13: How restricted Boltzmann machines learn<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1838 2022-09-30T12:23:24Z

<p>Michel.ferrero: /* Schedule Data-driven Physics */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Breaking news ==<br /> <br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions will be posted next week.<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn &lt;!-- ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions]) --&gt;<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1837 2022-09-30T12:21:33Z

<p>Michel.ferrero: /* Schedule Computational Physics */</p> <hr /> <div><br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Breaking news ==<br /> <br /> * MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here] (Google Colab/Jupyter Notebook). The solutions will be posted next week.<br /> <br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1831 2022-09-23T12:16:08Z

<p>Michel.ferrero: </p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1830 2022-09-23T12:15:16Z

<p>Michel.ferrero: </p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1829 2022-09-23T12:06:24Z

<p>Michel.ferrero: </p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> <br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1827 2022-09-16T13:06:37Z

<p>Michel.ferrero: /* Schedule Computational Physics */</p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 24.34.201. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version].<br /> <br /> &lt;!-- Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> --&gt;<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. <br /> <br /> &lt;!-- [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. --&gt;<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].<br /> &lt;!-- [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook] --&gt;<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] &lt;!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --&gt;<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] &lt;!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] --&gt;<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data]<br /> <br /> &lt;!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --&gt;<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1811 2022-09-09T14:52:11Z

<p>Michel.ferrero: /* Schedule Computational Physics */</p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 54.55.205. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 54 and tower 55 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1810 2022-09-09T07:32:54Z

<p>Michel.ferrero: /* Schedule Computational Physics */</p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 54.55.205. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 54 and tower 55 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1809 2022-09-07T22:26:03Z

<p>Michel.ferrero: /* Schedule Computational Physics */</p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * JUSSIEU salle 54.55.205. <br /> Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 54 and tower 55 of Jussieu campus.<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1804 2022-09-02T16:05:57Z

<p>Michel.ferrero: /* Schedule Computational Physics */</p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * CONDORCET salle 304 A<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1796 2022-09-01T14:51:55Z

<p>Michel.ferrero: /* Schedule Data-driven Physics */</p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices &lt;!-- ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]) --&gt;<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: <br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1795 2022-09-01T14:51:07Z

<p>Michel.ferrero: </p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices &lt;!-- ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]) --&gt;<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: <br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1794 2022-09-01T14:48:51Z

<p>Michel.ferrero: </p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices &lt;!-- ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]) --&gt;<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> &lt;!--<br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> --&gt;<br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: <br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1793 2022-09-01T14:47:19Z

<p>Michel.ferrero: </p> <hr /> <div>&lt;!-- This is a comment<br /> =Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> --&gt;<br /> <br /> This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.<br /> == Course description ==<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> &lt;!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --&gt;<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The [https://colab.research.google.com Colab platform] from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule Computational Physics ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices &lt;!-- ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]) --&gt;<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> &lt;!--<br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> --&gt;<br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule &lt;!-- ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) --&gt;<br /> <br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions &lt;!-- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --&gt;<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing &lt;!-- ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --&gt;<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: <br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms &lt;!--([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])--&gt;<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> == Schedule Data-driven Physics ==<br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> '''Friday, December 16, 2022'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> &lt;!-- '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --&gt;</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1769 2022-07-26T16:21:29Z

<p>Michel.ferrero: </p> <hr /> <div>=Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> <br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data]<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [ Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [XXX Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [XXX invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> &lt;!--<br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> --&gt;<br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: <br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> '''Friday, December 16, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&diff=1768 2022-07-26T16:19:47Z

<p>Michel.ferrero: </p> <hr /> <div>=Breaking news:=<br /> <br /> * Update with course schedule<br /> <br /> = Computational and Data Driven Physics =<br /> <br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data]<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> * [ Vincenzo Maria Schimmenti] (Tutor)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [XXX Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [XXX invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> * Homework 3: 5 points<br /> * Final exam in January: 15 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 2, 2022 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> &lt;!--<br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> --&gt;<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)<br /> <br /> '''Friday, September 9, 2022'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> <br /> '''Friday, September 16, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 23, 2022'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)<br /> <br /> '''Friday, September 30, 2022'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 7, 2022'''<br /> <br /> * Test: <br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, October 14, 2022'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> '''Friday, October 21, 2022'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> <br /> '''Friday, October 28, 2022'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> '''Friday, November 25, 2022'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> '''Friday, December 2, 2022'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> '''Friday, December 9, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> '''Friday, December 16, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])<br /> <br /> <br /> '''Final examination of the data-driven course (January 7, 2022)'''<br /> <br /> * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]<br /> <br /> * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]<br /> <br /> * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1725 2021-12-17T11:13:09Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>=Breaking news:=<br /> <br /> * Homework 1 has been evaluated and sent to you. If you did not receive it, please contact us. <br /> * Here you find the MCQ proposed last year <br /> [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]<br /> <br /> '''The Solution''' [https://colab.research.google.com/drive/1Faovrt0q5kRVtVsF9afr4iMXww_Wk53_?usp=sharing]<br /> <br /> = Computational and Data Driven Physics =<br /> <br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data]<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> <br /> <br /> <br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> <br /> '''Friday, November 12, 2021, 2 pm: The Quiz.'''<br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> <br /> MCQ Solution (correct answers in bold):[https://drive.google.com/file/d/1WWcqxFzmCJp2ZXC02a70a1o17pGupdyZ/view?usp=sharing]<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions])</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1724 2021-12-17T11:11:42Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>=Breaking news:=<br /> <br /> * Homework 1 has been evaluated and sent to you. If you did not receive it, please contact us. <br /> * Here you find the MCQ proposed last year <br /> [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]<br /> <br /> '''The Solution''' [https://colab.research.google.com/drive/1Faovrt0q5kRVtVsF9afr4iMXww_Wk53_?usp=sharing]<br /> <br /> = Computational and Data Driven Physics =<br /> <br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data]<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> <br /> <br /> <br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing]<br /> <br /> * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook].<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing]<br /> <br /> * Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. <br /> [https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]<br /> <br /> <br /> <br /> '''Friday, November 12, 2021, 2 pm: The Quiz.'''<br /> <br /> The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.<br /> <br /> <br /> MCQ Solution (correct answers in bold):[https://drive.google.com/file/d/1WWcqxFzmCJp2ZXC02a70a1o17pGupdyZ/view?usp=sharing]<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data] <br /> [https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]<br /> <br /> <br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Probabilistic graphical models<br /> <br /> * Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&amp;resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]<br /> <br /> * Tutorial 12: <br /> [https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]<br /> <br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([solutions])</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1597 2021-10-14T20:33:01Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>=Breaking news:=<br /> <br /> * Homework 1 has been evaluated and sent to you. If you did not receive it, please contact us. <br /> * Here you find the MCQ proposed last year <br /> [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]<br /> <br /> = Computational and Data Driven Physics =<br /> <br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> <br /> <br /> <br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> <br /> '''Friday, November 12, 2021, 2 pm: The Quiz.'''<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1589 2021-10-13T12:45:47Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> Here you find the MCQ proposed last year <br /> [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]<br /> <br /> <br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> --&gt;<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> <br /> '''Friday, November 12, 2021, 2 pm: The Quiz.'''<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1588 2021-10-13T12:44:50Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> Here you find the MCQ proposed last year <br /> [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]<br /> <br /> <br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> <br /> '''Friday, November 12, 2021, 2 pm: The Quiz.'''<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1587 2021-10-13T12:43:58Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> Here you find the MCQ proposed last year <br /> [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]<br /> <br /> <br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> <br /> * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e] solutions)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> <br /> '''Friday, November 12, 2021, 2 pm: The Quiz.'''<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1576 2021-10-08T12:27:41Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions.<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1575 2021-10-08T12:25:46Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6: Bayesian inference and single-particle tracking. Questions [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. Data [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. Starting Notebook [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions.<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1568 2021-10-07T10:53:19Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 6: Introduction to Bayesian inference<br /> <br /> * Tutorial 6:<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1567 2021-10-07T10:52:42Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1552 2021-10-01T07:24:01Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!<br /> <br /> * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1549 2021-09-23T10:14:02Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 5: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1548 2021-09-23T10:13:45Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 5: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1547 2021-09-22T11:33:06Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 5: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1546 2021-09-22T11:32:27Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices:<br /> * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 5: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1518 2021-09-17T14:44:18Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 5: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 7: Introduction to Bayesian inference<br /> <br /> * Tutorial 7:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 8: Asymptotic inference and information<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 9: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 10: Priors, regularisation, sparsity<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 11: Network inference<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 12: Supervised learning and phase transitions<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 13: Unsupervised learning and representations<br /> <br /> * Tutorial 13:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1517 2021-09-17T14:42:40Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 5]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 5: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * Tutorial 6: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1516 2021-09-17T14:41:35Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 6]: Optimization &amp; Dijkstra algorithm<br /> <br /> * Tutorial 6: Simulated annealing<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 6]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> --&gt;<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling<br /> <br /> * Tutorial 7: Faster than the clock algorithms<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 7]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> --&gt;<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1515 2021-09-17T14:39:44Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1514 2021-09-17T12:00:50Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1513 2021-09-17T10:18:51Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 24)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * Tutorial 4 - Ising model and phase transitions<br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> --&gt;<br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1509 2021-09-10T13:13:37Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 24)<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1508 2021-09-10T12:31:32Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1]<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1507 2021-09-10T12:28:26Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view Homework 1]<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri&diff=1487 2021-09-08T15:02:52Z

<p>Michel.ferrero: /* Schedule */</p> <hr /> <div>= Computational and Data Driven Physics =<br /> <br /> Modern physics is characterized by an increasing complexity of systems under investigation, in<br /> domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate<br /> models to describe these systems and being able to make quantitative predictions from those models<br /> is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.<br /> <br /> == Course description ==<br /> <br /> We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes. <br /> <br /> Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems. <br /> <br /> Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications<br /> borrowed from various domains of physics. We will focus on methods and algorithms and physics, not<br /> on programming and heavy numerics! You will have to hand in 3 homeworks.<br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)<br /> * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)<br /> * [http://www.lps.ens.fr/~cocco/ Simona Cocco] &amp; [https://www.pct.espci.fr/~david/ David Lacoste] &amp; [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 29 rue D'Ulm, salle Borel + Djebar<br /> <br /> == Slack ==<br /> <br /> If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the<br /> [http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack<br /> use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA invitation link].<br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Collaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> '''Computational Physics:'''<br /> * Homework 1: 5 points <br /> * Homework 2: 5 points<br /> * Multiple Choice Questions in November: 10 points<br /> <br /> '''Data Driven Physics:'''<br /> <br /> * Final exam in January: 20 points<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 3, 2021 '''<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> <br /> '''Friday, September 10, 2021'''<br /> <br /> * Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib]<br /> <br /> * Tutorial 2 - Markov matrices: [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx questions] and [https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]<br /> <br /> * Tutorial 3 - Thumb rule: [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU questions] and [https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]<br /> <br /> * Homework 1<br /> <br /> &lt;!--<br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> --&gt;<br /> <br /> '''Friday, September 17, 2021'''<br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> <br /> '''Friday, September 24, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> '''Friday, October 1, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> * [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> * Homework 2<br /> <br /> <br /> '''Friday, October 8, 2021'''<br /> <br /> * [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> '''Friday, October 15, 2021'''<br /> <br /> * Lecture 8: Introduction to Bayesian inference<br /> <br /> * Tutorial 8:<br /> <br /> <br /> '''Friday, October 22, 2021'''<br /> <br /> * Lecture 9: Asymptotic inference and information<br /> <br /> * Tutorial 9:<br /> <br /> <br /> '''Friday, October 29, 2021'''<br /> <br /> * Lecture 10: High-dimensional inference and Principal Component Analysis<br /> <br /> * Tutorial 10:<br /> <br /> <br /> '''Friday, November 26, 2021'''<br /> <br /> * Lecture 11: Priors, regularisation, sparsity<br /> <br /> * Tutorial 11:<br /> <br /> <br /> '''Friday, December 3, 2021'''<br /> <br /> * Lecture 12: Network inference<br /> <br /> * Tutorial 12:<br /> <br /> <br /> '''Friday, December 10, 2021'''<br /> <br /> * Lecture 13: Supervised learning and phase transitions<br /> <br /> * Tutorial 13:<br /> <br /> <br /> '''Friday, December 17, 2021'''<br /> <br /> * Lecture 14: Unsupervised learning and representations<br /> <br /> * Tutorial 14:</div>

Michel.ferrero