Classes will take place on Wednesdays from 9am to 12pm at ENS Cachan, in Amphi Curie.
Below is the tentative schedule, with scribe notes from last year that might be updated for some lectures.
Date | Lecturer | Topics | Corresponding chapters in class notes | Scribe notes |
September 30th |
Guillaume Obozinski | Introduction Maximum likelihood |
5 Slides (intro) Slides ML |
Huu Dien Khue Le, Robin Benesse lecture1.pdf lecture1.zip |
October 7th |
Guillaume Obozinski |
Linear regression Logistic regression Generative classification (Fisher discriminant) -- K-means EM Gaussian mixtures |
6, 7 Slides Regression -- 10, 11 Slides EM |
Aymeric Reshef, Claire Vernade lecture2.pdf lecture2.zip Marie d’Autume, Jean-Baptiste Alayrac lecture3.pdf lecture3.zip |
October 14th |
Simon Lacoste-Julien | Graph theory Directed graphical models Undirected graphical models |
2 |
Jaime Roquero, JieYing Wu (updated 11/29) lecture4.pdf lecture4.zip |
October 21st |
Simon Lacoste-Julien | Exponential families Information theory |
8, 19 scribbled notes |
Thomas Belhalfaoui, Lénaïc Chizat lecture5.pdf lecture5.zip |
October 28th |
Francis Bach | Gaussian variables Factor analysis |
13, 14 |
Lucas Plaetevoet, Ismael Belghiti lecture6.pdf lecture6.zip |
November 4th |
Simon Lacoste-Julien | Sum-product algorithm HMM |
4, 12 scribbled notes |
Pauline Luc, Mathieu Andreux lecture7.pdf lecture7.tex |
November 11th |
No lecture | |||
November 18th |
Guillaume Obozinski | Approximate inference I Sampling Variational inference |
21 |
Khalife Sammy, Maryan Morel (new!) lecture8.pdf lecture8.zip |
November 25th |
Guillaume Obozinski | Approximate inference II Sampling Variational inference |
21 |
Basile Clément, Nathan de Lara (new!) lecture9.pdf lecture9.tex |
December 2nd |
Simon Lacoste-Julien | Bayesian methods Model selection |
5.1 and 5.3 scribbled notes |
Gauthier Gidel, Lilian Besson (new!) lecture10.pdf lecture10.zip |
December 16th (Amphi Curie) |
Final Exam | |||
January 6th Batiment Cournot (C102-103) |
Project poster session |
Homework 1, due October 24th, 2015 (on the Moodle): Homework | Data.
Homework 2, due November 11th, 2015 (on the Moodle): Homework | Data
Homework 3, due January 6th, 2016 (on the Moodle; we highly recommend you submit it in December though): Homework | Data
The final project allows a further understanding of certain aspects of the course. The following schedule has to be respected.
November | Choose a project (one or two students per projects, preferably two) |
Before 11/18 | Send an email to the three teachers, in which all members of your team are cc'ed to request our agreement on your choice of team and project topic. |
Before 12/09 | Send a draft (1 page) + first results, on the Moodle. |
On 2016/01/06 | Poster session in Batiment Cournot (C102-103) - 9am to 12pm |
Before 2016/01/13 | Submit your project report (~6 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.
The course will be based on the book in preparation of Michael 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: December 2nd, 2015.