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/1SeT5S0gS5MFFStwccRQ1R5v9uuk8Qqq4/view?usp=sharing Questions].
[https://colab.research.google.com/drive/1DNHVysMQLdseIbgAEW_fgTXgngFqMg9-?usp=sharing Google Colab Starting Notebook].[https://colab.research.google.com/drive/1Wo4pkDfvzads4CNR2HibyeRTpIRYUqoq?usp=sharing Google Collab Solutions].[https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?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:Analysis of quantum trajectories of atoms in a cavity to infer the numbers of photons in the cavity. [https://drive.google.com/file/d/103-vCGVnY7QNUUo08f6mv6UlGmK85iZ6/view?usp=sharing Questions]. [https://drive.google.com/file/d/14bsrCZQVLY-vMqKAH8AFo7F2A1Jsz0Se/view?usp=sharing Google Collab Starting notebook].[https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1-jJBjR8wv2pfANoiWKXltEP0R1n9ZP1g/view?usp=sharing Solutions][https://colab.research.google.com/drive/1Wk0uH9UbHi3HkABVjBlzeTDFcncolVLN?usp=sharing=sharing Google Collab Solutions].


* 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: Replay of the neuronal activity during sleep after a task [https://drive.google.com/file/d/112_PGk57Ateg8UxHhS40maeZj5cNUn8G/view?usp=sharing Questions]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].

[https://colab.research.google.com/drive/1xxuQjJAYPJLXVqUXTm75dFdyn0vDxEzj?usp=sharing Starting Notebook on Google Colab].

'''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