This year, the lectures will be taught in English. Homeworks and projects may be submitted in English or in French. Most lectures will be given at ENS Paris Saclay on Wednedays (9-12am). Some lectures will be given via zoom. Aude Sportisse will be the new TA. Documents (slides, proofs, some video recordings) will be uploaded regularly here.
Below is the tentative schedule.
Date | Lecturer | Topics |
October 5th Amphi 0I10 (bâtiment Ouest, RdC) |
Pierre Latouche | Introduction Maximum likelihood Linear regression Logistic regression |
October 12th zoom |
Pierre Latouche |
K-means EM Gaussian mixtures |
October 19th zoom |
Pierre-Alexandre Mattei |
Directed graphical models Undirected graphical models |
November 9th Amphi 0I10 (bâtiment Ouest, RdC) |
Pierre Latouche | Bayesian linear regression Gaussian processes EM revisited Model selection |
November 16th zoom |
Pierre Latouche | Approximate inference I: variational techniques Bayesian GMM + VEM, Expectation propagation |
November 23th zoom |
Pierre-Alexandre Mattei |
Sum-product algorithm HMM |
December 7th zoom |
Pierre-Alexandre Mattei | Approximate inference II: Monte Carlo, MCMC |
December 14th Amphi 1G58 (bâtiment Sud, au dessus de l'accueil) |
Pierre-Alexandre Mattei | Approximate inference III: amortized variational
inference Deep latent variable models, Variational auto-encoders |
January 4th Amphi 0I10 (bâtiment Ouest, RdC) |
Pierre-Alexandre Mattei | Deep generative models beyond VAEs GAN, autoregressive models, normalizing flows, ... |
January 11th Amphi 0I10 (bâtiment Ouest, RdC) |
Final Exam ! |
This course provides a unifying introduction to probabilistic modelling through the framework of graphical models, together with their associated learning and inference algorithms. It is one of the few historical courses at the core of the MVA program. Recent developments in deep latent variable models (deep neural networks, variational autoencoders, generative adversarial networks, ...) are now part of the program of this course.
The course is based on the book Pattern Recognition and Machine Learning by C. Bishop . It is also based on the book Machine Learning a Probabilistic Perspective by Kevin P. Murphy.
Last updated: December 14th, 2022.