Florent Krzakala

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florent.krzakala@epfl.ch +41 21 693 31 87 http://florentkrzakala.com

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

+41 21 693 31 87
Office:  ELD 239
EPFL > STI > IEM > IDEPHICS1

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

+41 21 693 31 87
+41 21 693 31 15
Office:  ELD 239
EPFL > SB > IPHYS > IDEPHICS2

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

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

+41 21 693 31 87
Office:  ELD 239
EPFL > SB > SB-SPH > SPH-ENS

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

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

+41 21 693 31 87
Office:  ELD 239
EPFL > STI > STI-SEL > SEL-ENS

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

+41 21 693 31 87
Office:  ELD 239
EPFL > VPA-AVP-PGE > AVP-PGE-EDOC > EDPY-ENS

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Administrative data

Teaching & PhD

Teaching

Electrical and Electronics Engineering

Physics

PhD Students

Arnaboldi Luca, Dandi Yatin, Francazi Emanuele, Ghio Davide, Pesce Luca, Vilucchio Matteo,

Courses

Fundamentals of inference and learning

(Coursebook not yet approved by the section)

Statistical physics

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

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.