=Breaking news:=

* Homework 1 has been evaluated and sent to you. If you did not receive it, please contact us.
* Here you find the MCQ proposed last year
[https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz]

= 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.

== 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.pct.espci.fr/~david/ David Lacoste] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)

== 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
[http://computational-ozw2847.slack.com Computational and Data Driven Physics Slack]. In order to join the Slack
use the following [https://join.slack.com/t/computational-ozw2847/shared_invite/zt-vegija8y-cx_vbF2BI6FJewx4W6coSA 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:'''

* Final exam in January: 20 points

== Schedule ==

'''Friday, September 3, 2021 '''

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

* [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix

'''Friday, September 10, 2021'''

* 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]

* [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 2] - Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions])

* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions])

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



'''Friday, September 17, 2021'''

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

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

'''Friday, September 24, 2021'''

* [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])



'''Friday, October 1, 2021'''

* [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])


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

* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22)

'''Friday, October 8, 2021'''

* Lecture 6: Introduction to Bayesian inference

* Tutorial 6: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1q9Quq5mvJ4Avv2qWgzTocU-DVIxJIVWi/view?usp=sharing Questions]. [https://drive.google.com/file/d/1xYb4wfNBysse6Q7frTw_V9TPdUQU5lUx/view?usp=sharing Data]. [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. [https://colab.research.google.com/drive/1VePL9X1JT6yl8LmROeZyMyf4Epcu3yOL#scrollTo=1XQNGa_fyEkJ Google colab version]. Solutions [https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing]. Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]

'''Friday, October 15, 2021'''

* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling

* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions])



'''Friday, October 22, 2021'''

* 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 ]

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

'''Friday, October 29, 2021'''

* 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 12, 2021, 2 pm: The Quiz.'''

'''Friday, November 26, 2021'''

* Lecture 10: Priors, regularisation, sparsity

* Tutorial 10:

'''Friday, December 3, 2021'''

* Lecture 11: Network inference

* Tutorial 11:

'''Friday, December 10, 2021'''

* Lecture 12: Supervised learning and phase transitions

* Tutorial 12:

'''Friday, December 17, 2021'''

* Lecture 13: Unsupervised learning and representations

* Tutorial 13: