Probabilistic Generative Models

This is the website for the Probabilistic Generative Models course (TC4) of Master 2 AI (2020-2021).


Recently, generative models have (again) become a hot topic in machine learning thanks to recent advances in deep learning. One of the benefit of these models is their ability to generate new data, see for example:

Moreover, they can be used for semi-supervised learning, feature extraction via latent variables, …

In this course, we will first review the theoretical background required to understand modern generative models:

  • Probabilities,
  • Latent variables models,
  • Expectation Maximization algorithm,
  • Change of variable theorem,
  • Neural parameterization of probability distributions.

Based on this, we will study modern generative models based on neural networks:

  • Variational Auto-Encoders (VAEs),
  • Generative Adversarial Networks (GANs),
  • Flow models,
  • Energy networks.

Grading Scheme

  • 50%: Lab exercises
  • 50%: Exam


You can contact me at caio.corro@u-psud.fr, either in French or English, with a subject starting with [TC4]. Please, do not worry about typos or not being overly formal enough (just treat your instructors and colleagues with the same respect you would like to be treated).

WARNING: Each mail must discuss at most one point. Don’t send e-mails to several of my addresses. Thank you.