Institute for Advanced Simulation (IAS)

Jülich Supercomputing Centre (JSC)

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

Anfang

20.05.2025 07:00 Uhr

Ende

22.05.2025 11:00 Uhr

Kontakt

Dr. Alina Bazarova

ai-courses-jsc@fz-juelich.de

Zum Termin anmelden

(Kurs-Nr. 6820252 im Trainingsprogramm 2025 des Forschungszentrums Jülich)

Dieser Kurs findet als Online-Veranstaltung statt. Der Link zur Streaming-Plattform wird nur den angemeldeten Teilnehmern zur Verfügung gestellt.

Inhalt:

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

Contents level	in hours	in %
Beginner's contents:	0 h	0 %
Intermediate contents:	4.5 h	50 %
Advanced contents:	4,5 h	50 %
Community-targeted contents:	0 h	0 %

Voraussetzungen:

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).