NUMPHYsandML

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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

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 (10 points each) + 1 MCQ (30 points)


Here you can find a trial of the MCQ 10 Questions about numerical physics

you can find a short presentation video of the MCQ on the Dropbox by A. Rosso

Schedule

Project in Markox Chain Monte Carlo Manon Michel's Project


Friday, September 4, 2020


Friday, September 11, 2020



Friday, September 18, 2020

Homework: Download


Friday, September 25, 2020


  • Lecture 4: Ising model and phase transitions


Friday, October 2, 2020


Lecture 5: Quantum particle

Tutorial 5: Time evolution (quantum) problems

Homework 2: Download


Friday, October 9, 2020

Lecture 6: Importance sampling

Tutorial 6: Faster than the clock algorithms problems


Friday, October 16, 2020

GoToMeeting link [1] (Room 1 M2 ICFP)


Lecture 7: Optimization & Dijkstra algorithm

Tutorial 7: Simulated annealing problems



Friday, October 23, 2020

Lecture 8: Maximum Likelyhood estimation: follow the link [2]

Tutorial 8: Maximum Likelyhood estimation problems

Due: Homework 2 (send it to Marko)


Friday, November 06, 2020

Lecture 9: Restricted Boltzmann machines

Tutorial 9: Restricted Boltzmann machines problems


Friday, November 13, 2020

Tutorial 10: k-NN and regression problems

Homework 3 Due by December 4, 2020 Homework 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:

Lecture 11

The notes


Tutorial 11 Deep neural networks problems





Friday, December 4, 2020

Modern neural networks

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


Friday, December 11, 2020

Multiple Choice Questions: the final test

References