Introduction to Probabilistic Graphical Models

Francis Bach - Nicolas Chopin
  INRIA/ENS - ENSAE

Master recherche specialite "Mathématiques Appliquees",

Parcours M2 Mathématiques, Vision et Apprentissage (ENS Cachan), 1er semestre, 2018/2019


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.

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!

Dates of classes

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

Date Lecturer Topics Polycopié
chapters
Scribe notes
October 3th
Francis Bach Introduction
Maximum likelihood
Linear regression
Logistic regression
Generative classification (Fisher discriminant)
5
Slides (intro)
Slides ML
6, 7
Slides Regression
Huu Dien Khue Le, Robin Benesse
lecture1.pdf
lecture1.zip
Aymeric Reshef, Claire Vernade
lecture2.pdf
lecture2.zip
October 10th
Francis Bach K-means
EM
Gaussian mixtures
10, 11
Slides EM
Marie d'Autume, Jean-Baptiste Alayrac
lecture3.pdf
lecture3.zip
October 24th
Francis Bach Graph theory
Directed graphical models
Undirected graphical models
2
Jaime Roquero, JieYing Wu
lecture4.pdf
lecture4.zip
October 31th
Francis Bach Exponential families
Information theory
Gaussian variables
Factor analysis

8, 19

13, 14
Thomas Belhalfaoui, Lénaic Chizat
lecture5.pdf
lecture5.zip

Lucas Plaetevoet, Ismael Belghiti
lecture6.pdf
lecture6.zip

November 7th Nicolas Chopin Sum-product algorithm
HMM
4, 12
Pauline Luc, Mathieu Andreux
lecture7.pdf
lecture7.tex
November 14th Nicolas Chopin Approximate inference I
Sampling and MCMC methods
21
Khalife Sammy, Maryan Morel
lecture8.pdf
lecture8.zip
November 21th Nicolas Chopin Approximate inference II:
Variational inference
21
Basile Clément, Nathan de Lara
  lecture9.pdf
lecture9.tex
December 5th Nicolas Chopin Bayesian methods
Model selection
5.1 and 5.3
Gauthier Gidel, Lilian Besson
lecture10.pdf
lecture10.zip
December 12th

Final Exam



Homeworks

Homework 1, due October 24, 2018 [data] [pdf]

Homework 2

Homework 4



Last updated: October 10th, 2018.