PHYS-642 / 4 credits

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

Language: English

Remark: Next time: Spring 2025


Frequency

Every 2 years

Summary

This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and statitics.

Content

Note

Website of the lecture:  https://idephics.github.io/EPFLDoctoralLecture2023/

Mainly a theoretical course, with exercises in the analytical methods and usage of the related algorithms in high-dimensional problems in statistics, optimization and machine learning

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

 

Learning Prerequisites

Required courses

For physicists : PHYS 512 & a good knowlegde of statistical physics.
For mathematicians: Probability & Introductory statistical physics will be helpful
FOR CS/STI: Basic probability & Information theory/Entropy/Coding will be helpful

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
  • derive formulas and algorithms for their solution, using technics from statistical physics

Resources

Moodle Link

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

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