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

Elements of statistics for data science

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

This course covers the statistical physics approach to computer science problems ranging from graph theory and constraint satisfaction to inference and machine learning. In particular the replica and cavity methods, message passings algorithms, and analysis of the related phase transitions.

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.