Introduction to Bayesian Statistical Learning
5 May 2026 - 7 May 2026
The course consists of three parts. The first topic, normalizing flows, explores a class of generative models that facilitate likelihood-free inference. The second topic, diffusion models, introduces students to a powerful class of generative models that excel in modeling sequential data, as well as how they are related to Bayesian framework. The third topic, Gaussian processes, is a versatile tool for Bayesian inference and non-parametric modeling. Gaussian processes provide a flexible framework for modeling complex relationships between variables without assuming a specific functional form.
Practical information:
5 May 2026 - 7 May 2026
12 hours
Online
English
Target audience: PhD students and Postdocs
Want to register?
- Prerequisites: Participants should be familiar with principles of Bayesian modeling and AI models; A personal institutional email address is required
- Price: Price upon request
Georganiseerd door:
Main topics:
- Normalizing flows
- Diffusion models
- Gaussian Processes
- Running models on a Supercomputer
Related courses
Summer School on Security and Privacy in the Age of AI
8 September 2026
Summerschool - Heverlee - KU Leuven, UGent, VUB, Imec