Introduction to Bayesian Statistical Learning 2 (training course, online)

This course will take place as an online event. The link to the streaming platform will be provided to the registrants only.

Contents:

This course is the continuation of the course “Introduction to Bayesian Statistical Learning”. Although, participation in the latter is not strictly necessary to understand the material of this one, preliminary knowledge in Bayesian modelling, as well as in machine learning and artificial intelligence is a pre-requisite.

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.

The main topics are:

• Normalizing flows
• Diffusion models
• Gaussian Processes
• Running models on a Supercomputer

Prerequisites:

Participants should be familiar with principles of Bayesian modeling and AI models (e.g., participation in the course Introduction to Bayesian Statistical Learning I, or similar knowledge).

Target audience:

PhD students and Postdocs

Language:

This course is given in English.

Duration:

3 half days

Date:

20.-22. May 2025, 9:00 - 13:00

Venue:

Online

Number of Participants:

maximum 50

Instructor:

Dr. Alina Bazarova, JSC
Dr. Steve Schmerler, HZDR

Contact:

ai-courses-jsc@fz-juelich.de