Florent Krzakala

This is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternate, between mathematical theoretical foundations and practical computational aspects in python.

Statistical physics

PHYS-338

This course introduces the fundamental principles of statistical physics, one of the most fundamental theories of modern physics, focusing on the description of collective phenomena from microscopic laws.

Statistical physics for optimization & learning

PHYS-642

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.

Statistical physics of computation

PHYS-512

The students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and machine learning.