Introduction to Bayesian Statistical Learning (Trainingskurs, online)
Dr. Alina Bazarova
Der Kurs findet als Online-Veranstaltung statt. Der Link zur Online-Plattform wird nur den registrierten Teilnehmer:innen bekannt gegeben.
Inhalt:
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
Contents level | in hours | in % |
|---|---|---|
Beginner's contents: | 4.5 h | 30 % |
Intermediate contents: | 10.5 h | 70 % |
Advanced contents: | 0 h | 0 % |
Community-targeted contents: | 0 h | 0 % |
Voraussetzungen:
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.
Zielgruppe:
PhD students and Postdocs
Lernziel:
The ability to set up a Bayesian approach within a given framework
Sprache:
Der Kurs wird auf Englisch gehalten.
Dauer:
5 halbe Tage
Date:
18. - 22. März 2024, jeweils 9.00 - 13.00 Uhr
Ort:
Online
Anzahl der Teilnehmenden:
maximum 25
Referentin:
Dr. Alina Bazarova
Kontakt:
- Institute for Advanced Simulation (IAS)
- Jülich Supercomputing Centre (JSC)
Raum 3002
Prof. Dr. Stefan Kesselheim
Head of SDL Applied Machine Learning & AI Consultant team PI in Helmholtz Information Program 1, Topic 1
- Institute for Advanced Simulation (IAS)
- Jülich Supercomputing Centre (JSC)
Raum 3023
Anmeldung:
Bitte melden Sie sich bis zum 11. März 2024 über das Anmeldeformular an.