Graphical models (or probabilistic graphical models) provide a powerful
paradigm to jointly exploit probability theory and graph theory for solving
complex real-world 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
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 Gif-sur-Yvette campus of CentraleSupelec, unless mentioned otherwise below.
Mailing list: All announcements (last-minute changes, projects, etc.) will be made on a dedicated mailing list. Everyone who registered for the course has already been added to this list. If you are new, and would like to subscribe to the list, visit: https://sympa.inria.fr/sympa/subscribe/grmdil.
|11/1||13:45 - 17:00||Microsoft Teams||Introduction [slides]|
Graphical Models [slides]
|18/1||13:45 - 17:00||Amphi F2.07||Relaxation, Primal-dual methods [slides]|
|25/1||13:45 - 17:00||Microsoft Teams||Belief propagation [slides]|
Graph cuts - intro [slides]
|01/2||13:45 - 17:00||Amphi F2.07|
|22/2||13:45 - 17:00||Microsoft Teams||Graph Cuts [slides]|
|01/3||13:45 - 17:00||Microsoft Teams||Graph Cuts (contd.), Bayesian Nets [slides]|
|08/3||13:45 - 17:00||Amphi F2.07||Causality and other topics [slides]|
|01/4||10:30 - 17:00||Amphi E2.19 (Janet), Breguet||Project presentations|