= Numerical Physics and Machine Learning =

== Course description ==

We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.
We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.

'''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''

== The Team ==

* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)
* [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)
* Marko Medenjak (Tutorials)

== Where and When ==

* Lectures on Fridays: 14:00-16:00
* Tutorials on Fridays: 16:00-18:00
* ENS, 24 rue Lhomond, room L367 (third floor)

== 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 Colaboratory 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 ==

Homeworks (50% of the mark) + 1 MCQ (50% of the final mark)

== Schedule ==

'''Friday, September 4, 2020 '''

* [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 11, 2020'''

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

* [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]

'''Friday, September 18, 2020'''

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

* [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]

Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]

'''Friday, September 25, 2020'''

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

* [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems]

'''Friday, October 2, 2020'''

[https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle

[https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]

Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]

'''Friday, October 9, 2020'''

[https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling

[https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]

''' Friday, October 16, 2020'''

'''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)

[https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization & Dijkstra algorithm

[https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]

'''Friday, October 23, 2020'''

Lecture 8: Maximum Likelyhood estimation:

[https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]

''' Due: Homework 2 (send it to Marko)'''

'''Friday, November 06, 2020'''

Lecture 9: Restricted Boltzmann machines

[https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]

'''Friday, November 13, 2020'''

[https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]

'''Homework 3''' Due by December 4, 2020
[https://colab.research.google.com/drive/1NkJPKqkut-7Vbr-0jtiyi1tXarvxLUsU?usp=sharing Homework]
[https://drive.google.com/file/d/1Nts8Dc06QjZ7E2uFPj4D6YHVZTTgJeih/view?usp=sharing Data]

'''Friday, November 27, 2020'''

Tomorrow Florent cannot really give the talk in direct live ... but never fear:
* he can make a short Q/A tomorrow at, say, 15h or 15H30

* he registered the whole lecture in video, and put it here:

[https://www.dropbox.com/s/1yvmqbb5bb67w8n/video_lec4.mov?dl=0 Lecture 11]

[https://www.dropbox.com/s/dl31z2306y9salr/ML-lec4.pdf?dl=0 The notes]

[https://colab.research.google.com/drive/1OfxV5oL-9CVOxuhKgUF8AsboB89fm2xQ?usp=sharing Tutorial 11] Deep neural networks
[https://colab.research.google.com/drive/1Zblg4v9RE-zcIgIHI3It9Kw8EmzcvwjR?usp=sharing problems]

'''Friday, December 4, 2020'''

[https://colab.research.google.com/drive/1yxct9k6f2BioBQ6OywN22yfeDtGH_SlJ?usp=sharing Tutorial 12] Convolutional neural networks and auto-encoders

'''Due: Homework 3''' (send it to me (Marko))

'''Friday, December 11, 2020'''

'''Multiple Choice Questions: '''

-------------
'''The Solution''' [https://drive.google.com/file/d/1lMwtaAHerJ4uffZoE9egy7Pa_V2-cwOe/view?usp=sharing]

'''The test'''

The MCQ is composed of 20 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.

''' The Zoom link'''

Follow the link [https://zoom.us/j/4583355667?pwd=bUExNDJ1OU9IZEF6VUV5cmZKWDJ1dz09]. I will be there starting from 13h30, we will discuss the rules and I will be there to help you if you face a problem.

'''Time schedule'''

* The exam starts at 14h00: you download your file of questions from that dropbox directory that brings your name.

* Name the file with your answers as familyname_name.txt.

* The answers shuld be presented in the following way:

1 A

2 B

4 C

(if some question is missing - as question 3 here - it is not a problem)

* Send the file with your answers at numphys.icfp@gmail.com before 4 pm.

'''Rules'''

* You are allowed to use all material you think useful.

* You are not allowed to communicate with other people. Questions will be randomised to make hard life of cheaters, but please do not be one of them!

'''GOOD LUCK!'''

== References ==
* [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]
* Other references are specified in each lectures

CoDaDri

2021-10-14T06:40:27Z

Alberto: /* Breaking news: */

2021-10-12T14:58:40Z

Alberto: /* Grading */

= 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

Here you find the MCQ proposed last year
https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing

'''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

* Tutorial 7: Faster than the clock algorithms


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

* Lecture 8: Asymptotic inference and information

* Tutorial 8:

* 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

* Tutorial 9:

'''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: