Advanced Bayesian Learning - PhD course

NOTE: The topics, date and times have been updated for the 2024 edition of the course, but the material and exercises may/will change within the topics as we approach the course start.

Course information

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 Stockholm University, and to have some experience with programming.

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 given online using Zoom.

Welcome!

Mattias Villani
Professor of Statistics, Stockholm University

Topic 1 - Gaussian processes regression and classification

Reading: Gaussian Processes for Machine Learning - Chapters 1, 2.1-2.5, 3.1-3.4, 3.7, 4.1-4.3.
Code: GPML for Matlab | GPy for Python | Gausspr in R | Gaussianprocesses.jl in Julia | GPyTorch - GPs in PyTorch
Other material: Visualize GP kernels

Lecture 1 - May 3, hours 10-12
slides
Lecture 2 - May 3, hours 13-15
slides

Lab Topic 1
Problems | Lidar data

Topic 2 - Mixture models and Bayesian nonparametrics

Reading: Bayesian Data Analysis - Chapter 23 | The Neal (2000) article on MCMC for Dirichlet Process Mixtures

Lecture 3 - May 17, hours 10-12
slides
Lecture 4 - May 17, hours 13-15
slides | derivation marginal Gibbs

Lab Topic 2
Problems | Galaxy data

Topic 3 - Variational inference

Reading: Blei et al JASA | Tran's VI Notes
Other material: Natural gradient notes | autograd in python | ForwardDiff in Julia

Lecture 5 - May 31, hours 10-12
slides
Lecture 6 - May 31, hours 13-15
slides

Lab Topic 3
Problems | Time series data

Topic 4 - Bayesian regularization and variable selection

Reading (ordered by priority): TBD

Lecture 7 - June 14, hours 10-12
slides
Lecture 8 - June 14, hours 13-15
slides

Lab Topic 4
Problems

krishvishal / AdvBayesLearnCourse