Difference between revisions of "NUMPHYsandML"

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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.

Revision as of 14:11, 31 August 2020

Numerical Physics and Machine Learning

Course description

The Team

Where and When

  • Lectures on Fridays: 14.0-16.00
  • Tutorials on Fridays: 16h00-18.00
  • ENS, 24 rue Lhomond, room Conf IV (2nd 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.


3 homeworks (10 points each) + 1 MCQ (20 points) + 1 oral exam (50 points)


here it is please register