Wiki Cours - User contributions [en]

CoDaDri2

2022-10-19T10:03:53Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.

== Breaking news ==



== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & Jorge Fernandez de Cossio Diaz & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

* Solutions: You can find them [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers are marked in bold.

== Schedule Data-driven Physics ==

Book on "From Statistical Physics to Data-Driven Modelling" by S. Cocco, R. Monasson, F. Zamponi [https://drive.google.com/file/d/1_I1MML-Le8SlT-NF6XJB5Jm5MD5FW0Xz/view?usp=sharing File]

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference. Extra material: [https://drive.google.com/file/d/17VA5XWZ07aqWcvjQez64VqctAeWsJe69/view?usp=sharing Combinatorial identities by Hohle & Held 2006]

* 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].[https://colab.research.google.com/drive/1Wo4pkDfvzads4CNR2HibyeRTpIRYUqoq?usp=sharing Solutions]


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* Tutorial 13: How restricted Boltzmann machines learn

CoDaDri2

2022-10-07T12:19:27Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==





== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* Tutorial 13: How restricted Boltzmann machines learn

CoDaDri2

2022-10-07T12:19:16Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

<! -- * Exam: The MCQ is [https://drive.google.com/file/d/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv/view?usp=sharing here] -->



== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* Tutorial 13: How restricted Boltzmann machines learn

CoDaDri2

2022-10-07T12:19:08Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

<! --* Exam: The MCQ is [https://drive.google.com/file/d/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv/view?usp=sharing here] -->



== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* Tutorial 13: How restricted Boltzmann machines learn

CoDaDri2

2022-10-07T12:14:27Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

* Exam: The MCQ is [https://drive.google.com/file/d/1MFgI6lin35KV8ahunofsSfSxMg7tgfXv/view?usp=sharing here]



== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* Tutorial 13: How restricted Boltzmann machines learn

CoDaDri2

2022-10-01T11:44:08Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

* 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).

== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn

CoDaDri2

2022-10-01T11:43:26Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

* 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. The solutions can be found [https://drive.google.com/file/d/1VkPOa2rc0npZf5nMMfaTxf_ou4ObRMSv/view?usp=sharing here].

== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn

CoDaDri2

2022-09-23T17:01:26Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

* 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.

== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-23T16:52:15Z

VincenzoSchimmenti: /* Breaking news */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

* 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 in two weeks.

== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-23T16:51:47Z

VincenzoSchimmenti:

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Breaking news ==

* MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here]. The solutions will be posted in two weeks.

== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-23T16:51:27Z

VincenzoSchimmenti:

== Breaking news ==

* MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here]. The solutions will be posted in two weeks.

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-23T16:51:11Z

VincenzoSchimmenti:

=Breaking news:=

* MCQ of last year (2021/2022) can be found [https://drive.google.com/file/d/1wQQ8uhYvuYoxNvfr-O6QnyJwSEbYAoLF/view?usp=sharing here]. The solutions will be posted in two weeks.

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 24.34.201.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [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])

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].


'''Friday, October 21, 2022'''

* 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]

* 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].


* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-11T14:43:03Z

VincenzoSchimmenti: /* Schedule Data-driven Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 54.55.205.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms 

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions 

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].



* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].



'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] 

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-11T14:42:43Z

VincenzoSchimmenti: /* Schedule Data-driven Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 54.55.205.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms 

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions 

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].



* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].



'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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] 

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-11T14:42:09Z

VincenzoSchimmenti: /* Schedule Data-driven Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* JUSSIEU salle 54.55.205.
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.

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]

'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars]

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms 

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions 

* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 

'''Friday, October 7, 2022'''

* 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.

== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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].



* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].



'''Friday, October 28, 2022'''

* 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]

* 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].


'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]



'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-01T11:37:09Z

VincenzoSchimmenti: /* Schedule Data-driven Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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]) -->

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule 

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions 



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 



'''Friday, October 7, 2022'''

* Test:

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.

* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms 



== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2022'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-01T11:36:23Z

VincenzoSchimmenti: /* Schedule Computational Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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]) -->

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule 

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions 



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 



'''Friday, October 7, 2022'''

* Test:

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.

* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 6]: Faster than the clock algorithms 



== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-01T11:35:50Z

VincenzoSchimmenti: /* Schedule Computational Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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]) -->

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule 

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions <! -- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) -->



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-01T11:35:37Z

VincenzoSchimmenti: /* Schedule Computational Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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]) -->

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule <! --([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) -->

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions <! -- ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) -->



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing 



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-01T11:33:39Z

VincenzoSchimmenti: /* Schedule Dara-driven Physics */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



== Schedule Data-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

CoDaDri2

2022-09-01T11:33:23Z

VincenzoSchimmenti: /* Schedule */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule Computational Physics ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



== Schedule Dara-driven Physics ==

'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:31:22Z

VincenzoSchimmenti: /* Computer Requirements */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:31:08Z

VincenzoSchimmenti: /* Computer Requirements */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
The [https://colab.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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:30:59Z

VincenzoSchimmenti: /* Computer Requirements */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
The [colab.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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:30:02Z

VincenzoSchimmenti: /* Course description */

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.



== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:26:49Z

VincenzoSchimmenti:

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:15:15Z

VincenzoSchimmenti:

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:15:00Z

VincenzoSchimmenti: /* Course description */

This is the official page for the year 2022-2023 of the Computational and Dara-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:14:48Z

VincenzoSchimmenti:

This is the official page for the year 2022-2023 of the Computational and Dara-Driven Physics (CoDaDri) course.
== Course description ==

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:14:06Z

VincenzoSchimmenti:

This is the official page for the year 2022-2023 of the Computational and Dara-Driven Physics (CoDaDri) course. Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

== Course description ==

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:12:42Z

VincenzoSchimmenti:

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

== Course description ==

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:11:44Z

VincenzoSchimmenti:

%=Breaking news:=
%
%* Update with course schedule

= Computational and Data Driven Physics =

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

== Course description ==

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]

CoDaDri2

2022-09-01T11:11:12Z

VincenzoSchimmenti: /* The Team */

=Breaking news:=

* Update with course schedule

= Computational and Data Driven Physics =

Modern physics is characterized by an increasing complexity of systems under investigation, in
domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate
models to describe these systems and being able to make quantitative predictions from those models
is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

== Course description ==

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.

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.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not
on programming and heavy numerics! You will have to hand in 3 homeworks.

*[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]

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics)
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data-driven physics)
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)
* [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 29 rue D'Ulm, salle Borel + Djebar

== Slack ==

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the
[XXX Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [XXX invitation link].

== Computer Requirements ==

'''No previous experience in programming is required.''' 

'''Programming Language: Python'''

For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. 
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.

== Grading ==

'''Computational Physics:'''
* Homework 1: 5 points
* Homework 2: 5 points
* Multiple Choice Questions in November: 10 points

'''Data Driven Physics:'''
* Homework 3: 5 points
* Final exam in January: 15 points

== Schedule ==

'''Friday, September 2, 2022 '''

* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo

* [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])

* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23)

* 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]



'''Friday, September 9, 2022'''

* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

'''Friday, September 16, 2022'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling

* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Tutorial 3]: Errors and Precision

'''Friday, September 23, 2022'''

* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions

* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions])



* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 14)

'''Friday, September 30, 2022'''

* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm

* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions])



'''Friday, October 7, 2022'''

* Test:

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.

* [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])



'''Friday, October 14, 2022'''

* Lecture 7: Introduction to Bayesian inference

* 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]

* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

'''Friday, October 21, 2022'''

* 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]

* 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].

'''Friday, October 28, 2022'''

* 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]

* 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].
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]

'''Friday, November 25, 2022'''

* Lecture 10: Priors, regularisation, sparsity

* 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]
[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 ]

'''Friday, December 2, 2022'''

* Lecture 11: Probabilistic graphical models

* 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]

'''Friday, December 9, 2021'''

* Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ]

* Tutorial 12:
[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]

'''Friday, December 16, 2021'''

* Lecture 13: Unsupervised learning and representations

* [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])

'''Final examination of the data-driven course (January 7, 2022)'''

* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing]

* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing]

* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing]