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TUtorial 9: Restricted Boltzmann machines
TUtorial 9: Restricted Boltzmann machines
'''Friday, December 11, 2020'''
'''Friday, December 11, 2020'''

Revision as of 18:07, 15 October 2020

Numerical Physics and Machine Learning


Starting from Friday (25-09-2020) we are changing lecture&tutorial's room. Our new room is L367 (third floor)

The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK.

If you want to come on Friday, we ask you to write your name in the list below:

List of participants

If you do not find a place, please let us know. Thank you very much for your cooperation!

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.

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.


Homeworks (10 points each) + 1 MCQ (20 points)


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

Friday, October 23, 2020

Lecture 8: Maximum Likelihood estimation

Tutorial 8: Maximum Likelihood estimation

Due: Homework 2

Friday, November 06, 2020

Lecture 9: Restricted Boltzmann machines

TUtorial 9: Restricted Boltzmann machines

Friday, December 11, 2020

Multiple Choice Questions: the final test