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 (Grand-Amphi 1G58) on Wednedays (9-12am). Otmane Sakhi will be the TA. Documents (slides, proofs, some video recordings) are uploaded regularly here.
Below is the tentative schedule.
Date | Lecturer | Topics |
October 6th Le Grand-Amphi 1G58 |
Pierre Latouche | Introduction Maximum likelihood Linear regression Logistic regression |
October 13th zoom click here |
Pierre Latouche |
K-means EM Gaussian mixtures |
October 20th Le Grand-Amphi 1G58 |
Pierre Latouche | Bayesian linear regression Gaussian processes EM revisited Model selection |
November 10th Le Grand-Amphi 1G58 |
Pierre Latouche | Approximate inference I: variational techniques Bayesian GMM + VEM, Expectation propagation |
November 24th Le Grand-Amphi 1G58 |
Pierre-Alexandre Mattei |
Directed graphical models Undirected graphical models |
November 29th zoom click here |
Pierre-Alexandre Mattei |
Sum-product algorithm HMM |
December 1st zoom click here Warnings: this lecture will be given between 2 and 5pm ! |
Pierre-Alexandre Mattei | Approximate inference II: Monte Carlo, MCMC |
December 8th Le Grand-Amphi 1G58 |
Pierre-Alexandre Mattei | Approximate inference III: amortized variational
inference Deep latent variable models, Variational auto-encoders |
December 15th Le Grand-Amphi 1G58 |
Pierre-Alexandre Mattei | Deep generative models beyond VAEs GAN, autoregressive models, normalizing flows, ... |
January 12th Le Grand-Amphi 1G58 |
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.
The course will be based on the book Pattern Recognition and Machine Learning by C. Bishop Book
Last updated: December 10th, 2021.