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.

Team

Schedule

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

Here you find the scheduling of Lectures, Tutorials and Homeworks


Language

  • The working language for this course is English.
  • Programming Language: Python. See Memento Python
For practical installation, we recommand (especially for apple computers) Anaconda. 

No previous experience in programming is required.

  • Python notebooks on your computer are great. But another possibility, and quite good way to use powerful computer without buying one, is to use google colab, the Colaboratory platform from Google: 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

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

WIFI

The WIFI network is: PHYS-GUEST

The WIFI password for this networks is: PhysiqueENS

Then you can run a navigator (Firefox, Chrome, IE, Safari...)

Accept the certificat and enter the password: numerical.physics@phys.ens.fr


Forum

here it is please register


References