Graphical models (or probabilistic graphical models) provide a powerful
paradigm to jointly exploit probability theory and graph theory for solving
complex realworld problems. They form an indispensable component in several
research areas, such as statistics, machine learning, computer vision, where a
graph expresses the conditional (probabilistic) dependence among random
variables.
This course will focus on discrete models, that is, cases where the random variables of the graphical models are discrete. After an introduction to the basics of graphical models, the course will then focus on problems in representation, inference, and learning of graphical models. We will cover classical as well as state of the art algorithms used for these problems. Several applications in machine learning and computer vision will be studied as part of the course. 

All the classes will be held at the GifsurYvette campus of CentraleSupelec, in EB.114 or EB.106, Eiffel building. 
14/1  13:45  17:00  EB.114  Introduction to the course [slides] Graphical Models [slides] 
04/2  13:45  17:00  EB.106  Belief Propagation [slides] Introduction to Graph Cuts [slides] 
11/2  13:45  17:00  EB.106  Graph cuts + Primaldual, Part I 
18/2  13:45  17:00  EB.106  Graph cuts + Primaldual, Part II 
25/2  13:45  17:00  EB.106  Recommender systems + 2 papers 
03/3  13:45  17:00  EB.106  Causality [slides] 
10/3  13:45  17:00  EB.106  Learning [slides] 
12/3  08:30  11:30  EB.114  Bayesian Networks [slides] 
17/3  13:45  17:00  remote  Project presentations 