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 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.
 
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.
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'''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/lecture room.'''
  
  

Revision as of 11:48, 19 October 2020

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/lecture room.


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 (20 points)

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 Likelihood estimation

Tutorial 8: Maximum Likelihood estimation problems

Due: Homework 2 (send it to Marko)


Friday, November 06, 2020

Lecture 9: Restricted Boltzmann machines

TUtorial 9: Restricted Boltzmann machines

Friday, December 11, 2020

Multiple Choice Questions: the final test

References