This course provides a unifying introduction to probabilistic modelling through the framework of graphical models,together with their associated learning and inference algorithms.
Classes will take place on Wednesdays from 9am to 12pm at ENS Cachan, in Amphi Curie.
Moodle for the class. Now active!
Below is the tentative schedule, with scribe notes from last years that might be updated for some lectures.
Generative classification (Fisher discriminant)
|Huu Dien Khue Le, Robin Benesse
Aymeric Reshef, Claire Vernade
||Francis Bach|| K-means
| Marie d'Autume, Jean-Baptiste Alayrac
||Francis Bach||Graph theory
Directed graphical models
Undirected graphical models
|Jaime Roquero, JieYing Wu
||Francis Bach||Exponential families
|Thomas Belhalfaoui, Lénaic Chizat
Lucas Plaetevoet, Ismael Belghiti
|November 7th||Nicolas Chopin||Sum-product algorithm
|Pauline Luc, Mathieu Andreux
|November 14th||Nicolas Chopin||Approximate inference I
Sampling and MCMC methods
|Khalife Sammy, Maryan Morel
|November 21th||Nicolas Chopin||Approximate inference II:
| Basile Clément, Nathan de Lara
|December 5th||Nicolas Chopin||Bayesian methods
|Gauthier Gidel, Lilian Besson
If you are not registered in the moodle, please send the homeworks (one
pdf and one zip file for the code) to firstname.lastname@example.org
Homework 1, due October 24, 2018 [data]
Homework 2, due November 7, 2018 [data] [pdf] [solution] [matlab code]
Homework 3, due December 5, 2018 [data] [pdf]
Exam with solution [pdf]
Internships and PhD programs
Here are a few offers that we have received related to the class. There are many others within the MVA internship forum.
Last updated: December 20th, 2018.