Introduction to Probabilistic Graphical Models

Guillaume Obozinski - Francis Bach
Ecole des Ponts, ParisTech - INRIA/ENS

Master recherche specialite "Mathématiques Appliquees",
Parcours M2 Mathématiques, Vision et Apprentissage (ENS Cachan), 1er semestre, 2017/2018

IMPORTANT: [ [ [ Please fill this form to be registered in the Moodle for the class.] ] ]

This year, the class will be taught in English. Homeworks and project may be submitted in English or in French.

Classes will take place on Wednesdays from 9am to 12pm at ENS Cachan, in Amphi Curie.

Moodle for the class. Now active!

Dates of classes

Below is the tentative schedule, with scribe notes from last year that might be updated for some lectures.

Date Lecturer Topics Polycopié
Scribe notes
October 4th
Francis Bach Introduction
Maximum likelihood
Linear regression
Logistic regression
Generative classification (Fisher discriminant)
Slides (intro)
Slides ML
6, 7
Slides Regression
Huu Dien Khue Le, Robin Benesse
Aymeric Reshef, Claire Vernade
October 11th
Francis Bach K-means
Gaussian mixtures
10, 11
Slides EM
Marie d'Autume, Jean-Baptiste Alayrac
October 18th
Guillaume Obozinski Graph theory
Directed graphical models
Undirected graphical models
Jaime Roquero, JieYing Wu
October 25th
Guillaume Obozinski Exponential families
Information theory
8, 19
Thomas Belhalfaoui, Lénaic Chizat
November 8th Guillaume Obozinski Gaussian variables
Factor analysis
13, 14
Lucas Plaetevoet, Ismael Belghiti
November 15th

Amphi Cauchy
Bat. Carnot
Ecole des Ponts

(info accès)
Francis Bach Sum-product algorithm
4, 12
Pauline Luc, Mathieu Andreux
November 22nd
Francis Bach Approximate inference I
Sampling and MCMC methods
Khalife Sammy, Maryan Morel
November 29th
Guillaume Obozinsi Approximate inference II:
Variational inference
Basile Clément, Nathan de Lara
December 20th
Guillaume Obozinski Bayesian methods
Model selection
5.1 and 5.3
Gauthier Gidel, Lilian Besson
January 9th
(Amphi Curie)

Final Exam

Homework (tentative schedule)

Homework 1, due date October 18th 2017 (to be submitted on the Moodle): Homework | Data. Now available via the links.

Paper reading

Reading proposals.

The goal of the paper reading is to further the understanding of certain aspects of the course. The following schedule has to be respected.

mid - November Choose a reading and form a team of three.
Before November 30th Send an email to the two teachers, in which all members of your team are cc'ed to request our agreement on your choice of team and reading topic.
Before December 20th Submit a first brief summary of 1 page on your reading.
Before January 16th Submit your reading report (max 4 pages, on the Moodle)


This course provides a unifying introduction to probabilistic modelling through the framework of graphical models, together with their associated learning and inference algorithms.

References - Class notes

The course will be based on the book in preparation of Michael I. Jordan (UC Berkeley). Printed version of parts of the book (playing the role of the "polycopie") will be available from the Master's administrative assistant one or two weeks after the beginning of classes. We will notify you when they are ready for you to go and pick them up.

Last updated: October 5th, 2017.