Course

PHYS-642: Statistical physics for optimization & learning

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.

Lectures

Instructors (3)

Florent Gérard Krzakala

Damien Yves Valery Barbier

Ludovic Théo Stephan

Official source

https://edu.epfl.ch/coursebook/en/statistical-physics-for-optimization-learning-PHYS-642

Moodle Page

http://go.epfl.ch/PHYS-642

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Summary

Lectures

Instructors (3)

Florent Gérard Krzakala

Damien Yves Valery Barbier

Ludovic Théo Stephan

Official source

https://edu.epfl.ch/coursebook/en/statistical-physics-for-optimization-learning-PHYS-642

Moodle Page

http://go.epfl.ch/PHYS-642

About this result

Lectures in this course (44)

Curie Weiss Model

Covers the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.

Graph Coloring III

Explores properties of clusters and colorability threshold in graph coloring, including average connectivity and rigidity.

Constraint Satisfaction Problem

Covers the Random Constraint Satisfaction Problem and explores methods for solving constraint satisfaction problems.

Large number of colors

Explores the relationship between colors and condensation in clustering algorithms.

Survey Propagation: Cavity Method

Explores the Survey Propagation algorithm and its application in solving constraint satisfaction problems.

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