Florent Krzakala

photo placeholder image

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

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

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

EPFL SB IPHYS IDEPHICS2
ELD 239 (Bâtiment ELD)
Station 11
CH-1015 Lausanne

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

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

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

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

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

vCard
Administrative data

Teaching & PhD

Teaching

Electrical and Electronics Engineering

Physics

PhD Programs

Doctoral program in computer and communication sciences

Doctoral Program in Electrical Engineering

Doctoral Program in Physics

PhD Students

Francazi Emanuele, Ghio Davide, Pesce Luca,

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.

Quantum physics II

The aim of this course is to introduce the concepts, methods and consequences of quantum physics. In particular, the angular momentum, perturbation theory, many-particle systems, quantum correlations (entanglement), open quantum systems, symmetries and invariance laws, will be addressed

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 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.