PHYS-642 / 4 credits

Teacher(s): Krzakala Florent Gérard, Zdeborová Lenka, Loureiro Bruno, Saglietti Luca

Language: English

Remark: Next time: Spring 2021


Frequency

Every 2 years

Summary

This course covers the statistical physics approach to computer science problems ranging from graph theory and constraint satisfaction to inference and machine learning. In particular the replica and cavity methods, message passings algorithms, and analysis of the related phase transitions.

Content

Note

Website of the lecture:  https://sphinxteam.github.io/EPFLDoctoralLecture2021/

Mainly theory course, with exercises in the analytical methods and usage of the related algorithms.

Evaluation of the lecture based on homeworks given during the whole semester

Keywords

Statistical physics, replica method, cavity method, neural networks, theory of machine learning, combinatorial optimization, community detection, graphical models, message passins algorithms.

Learning Prerequisites

Required courses

Basic probability and/or statistical physics

Learning Outcomes

By the end of the course, the student must be able to:

  • To study a range of problems in computer science and learning, and derive formulas and algorithms for their solution, using technics from statistical physics.

In the programs

  • Exam form: During the semester (session free)
  • Subject examined: Statistical physics for optimization & learning
  • Lecture: 28 Hour(s)
  • Exercises: 28 Hour(s)
  • Exam form: During the semester (session free)
  • Subject examined: Statistical physics for optimization & learning
  • Lecture: 28 Hour(s)
  • Exercises: 28 Hour(s)

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22