INRIA/ENS - ENSAE

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!

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 | |

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

Homework 2

Homework 4

* Last updated: October 10th, 2018. *