Florent Krzakala
EPFL STI IEM IDEPHICS1
ELD 239 (Bâtiment ELD)
Station 11
1015 Lausanne
+41 21 693 31 15
+41 21 693 31 87
Office: ELD 239
EPFL › SB › IPHYS › IDEPHICS2
Website: 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
Website: 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 › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDPY-ENS
Teaching & PhD
PhD Students
Jivan Waber, Luca Pesce, Yizhou Xu, Emanuele Francazi, Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi, Hugo Jules Tabanelli
Past EPFL PhD Students
Courses
Fundamentals of inference and learning
EE-411
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.