Florent Krzakala

EPFL STI IEM IDEPHICS1
ELD 239 (Bâtiment ELD)
Station 11
1015 Lausanne

EPFL STI IEM IDEPHICS1
ELD 239 (Bâtiment ELD)
Station 11
1015 Lausanne

Web site:  Web site:  https://www.epfl.ch/labs/idephics/

EPFL STI IEM IDEPHICS1
ELD 239 (Bâtiment ELD)
Station 11
1015 Lausanne

Web site:  Web site:  https://sph.epfl.ch/

EPFL STI IEM IDEPHICS1
ELD 239 (Bâtiment ELD)
Station 11
1015 Lausanne

EPFL STI IEM IDEPHICS1
ELD 239 (Bâtiment ELD)
Station 11
1015 Lausanne

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Administrative data

Teaching & PhD

Teaching

Electrical and Electronics Engineering

Physics

Courses

Fundamentals of inference and learning

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 of computation

Interest in the methods and concepts of statistical physics is rapidly growing in fields as diverse as theoretical computer science, probability theory, machine learning, discrete mathematics, optimization, signal processing and others. Large part of the related work has relied on the use of message-passing algorithms and their connection to the statistical physics of glasses and spin glasses.

Statistical physics for optimization & learning

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.