NUMPHYsandML

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.

The Team

• Alberto Rocco (Numerical Physics)
• Florent Krzakala (Machine Learning)
• Marco Medjnak (Tutorials)

= 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.

Grading

3 homeworks (10 points each) + 1 MCQ (20 points) + 1 oral exam (50 points) [?? to be decided]

Schedule

Friday, September 4, 2020

Lecture 1: Introduction to Monte Carlo

Friday, September 11, 2020

Tutorial 1: Markov matrix & thumb rule

Homework 1: Page Rank & Errors

Friday, September 18, 2020

Lecture 2: Error evaluation

Tutorial 2: Sampling non uniform distribution

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Friday, September 25, 2020

Lecture 2: Ising model and phase transitions

Tutorial 2: Sampling non uniform distribution

Friday, October 2, 2020

Friday, October 9, 2020

References

• SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)
• Other references are specified in each lectures