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Course material for the upcoming PhD course in Advanced Bayesian Learning
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README.md

README.md

Advanced Bayesian Learning - PhD course


Course information

This is the website for the PhD course Advanced Bayesian Learning.

The typical participant is a PhD student in Statistics or related fields (Mathematical Statistics, Engineering Science, Quantitative Finance, Computer Science, ...). The participants are expected to have taken a basic course in Bayesian methods, for example Bayesian Learning at Linköping University or Bayesian Statistics I at Stockholm University.

Examination and Grades: The course is graded Pass or Fail. Examination is through individual reports on distributed problems for each topic. Many of the problems will require computer implementations of Bayesian learning algorithms.

Course organization The course is organized in four topics, each containing four lecture hours. Course participants will spend most of their study time by solving the problem sets for each topic on their own computers without supervision.

All lectures are at Stockholm University, B-building in room B705 (at the Department of Statistics).

Welcome!

Mattias Villani
Professor of Statistics, Stockholm and Linköping University


Topic 1 - Gaussian Processes with Applications

Lecture 1 - April 17, hours 10-12
Reading: Code:

Lecture 2 - April 17, hours 13-15
Reading: Code:

Problems Topic 1
Problems topic 1


Topic 2 - Bayesian Nonparametrics

Lecture 3 - April 28, hours 10-12
Reading: Code:

Lecture 4 - April 28, hours 13-15
Reading: Code:

Problems Topic 2
Problems topic 2


Topic 3 - Variational Inference

Lecture 5 - May 15, hours 10-12
Reading: Code:

Lecture 6 - May 15, hours 13-15
Reading: Code:

Problems Topic 3
Problems topic 3


Topic 4 - Bayesian Model Inference

Lecture 7 - May 29, hours 10-12
Reading: Code:

Lecture 8 - May 29, hours 13-15
Reading: Code:

Problems Topic 4
Problems topic 4

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