Introduction to Bayesian Statistical Learning (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:

When observing data, the key question is: What I can learn from the observation? Bayesian inference treats all parameters of the model as random variables. The main task is to update their distribution as new data is observed. Hence, quantifying uncertainty of the parameter estimation is always part of the task. In this course we will introduce the basic theoretical concepts of Bayesian Statistics and Bayesian inference. We discuss the computational techniques and their implementations, different types of models as well as model selection procedures. We will exercise on the existing datasets use the PyMC3 framework for practicals.

The main topics are:

• Bayes theorem
• Prior and Posterior distributions
• Computational challenges and techniques: MCMC, variational approaches
• Models: Mixture Models, Bayesian Neural Networks, Variational Autoencoder, Normalizing Flows
• PyMC3 framework for Bayesian computation
• Running Bayesian models on a Supercomputer

Prerequisites:

Participants should be familiar with general statistical concepts, such as distributions, samples. Furthermore, familiarity with fundamental Machine Learning concepts such as regression, classification and training is helpful.

Target audience:

PhD students and Postdocs

Learning outcome:

The ability to set up a Bayesian approach within a given framework

Language:

This course is given in English.

Duration:

5 half days

Date:

18-22 March 2024, 9:00 - 13:00

Venue:

Online

Number of Participants:

maximum 25

Instructor:

Dr. Alina Bazarova

Contact:

ai-courses-jsc@fz-juelich.de

Alina Bazarova

• Institute for Advanced Simulation (IAS)
• Jülich Supercomputing Centre (JSC)

Building 14.14 /
Room 3002

+49 2461/61-1234

E-Mail

Registration:

Please register via the registration form until 11 March 2024.