Lectures will be given on Wednedays (9-12am). This year, the students will be evaluated by a [report + poster presentation] on a research paper. All lectures will be on zoom and recorded. The lectures at ENS Paris Saclay will be used to answer questions in person, regarding the lectures and the project. All documents (slides / blackboard / lecture notes). The teaching assistant for this course is Rémi Khellaf.
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 such as deep neural networks, variational autoencoders, generative adversarial networks are now part of the program of this course.
The course is based on the Pattern Recognition and Machine Learning book by C. Bishop . It is also based on the books Probabilistic Machine Learning: An Introduction and Probabilistic Machine Learning: Advanced Topics by K. Murphy.
| Date | Lecturer | Topics |
| October 2nd ENS Paris Saclay, amphi Alain Aspect 1G58 - bât Sud au dessus de l'accueil + zoom |
Pierre-Alexandre Mattei | Introduction to directed graphical models Maximum likelihood Exemples (linear regression, logistic regression, neural networks) |
| October 9th zoom |
Pierre Latouche |
K-means EM Gaussian mixtures PPCA |
| October 16th ENS Paris Saclay, amphi Hodgkin 0I10 - bât Ouest RdC + zoom |
Pierre Latouche | Bayesian linear regression Gaussian processes EM revisited Model selection |
| October 23rd zoom |
Pierre Latouche |
Approximate inference I: variational techniques Stochastic block models + VEM Expectation propagation |
| November 6th ENS Paris Saclay, amphi d'Alembert 1Z18 - bât Nord + zoom |
Pierre-Alexandre Mattei |
Approximate inference II: amortized variational
inference Deep latent variable models, variational auto-encoders |
| November 13th zoom |
Pierre Latouche | Directed graphical models: theory and examples |
| November 20th ENS Paris Saclay, amphi Hodgkin 0I10 - bât Ouest RdC + zoom |
Pierre-Alexandre Mattei | Undirected graphical models Sum-product algorithm HMM |
| November 26th zoom |
Pierre-Alexandre Mattei | Approximate inference III: Monte Carlo, MCMC, energy based models, score based diffusion models |
| December 11th zoom |
Pierre-Alexandre Mattei | Deep generative models beyond VAEs GAN, autoregressive models, normalizing flows, diffusion models |
| December 18th ENS Paris Saclay (atrium) |
Poster session |
Last updated: November 4th, 2024.