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:
- face generation: https://thispersondoesnotexist.com/
- word generation: https://thisworddoesnotexist.com/
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:
- 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.
- 50%: Lab exercises
- 50%: Exam
You can contact me at firstname.lastname@example.org, 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.