Advanced Bayesian Learning, 7.5 credits

This is an advanced course in Bayesian statistics for PhD students in statistics, computer science, the engineering sciences and other related fields.
The course is divided into 4 contemporary topics in Bayesian analysis, and the choice of topics can vary from year to year depending on the research frontier. Students can pick and choose among the topics and will be given 2 credits for each selected topic.
The planned topics for the current year are:

1. Gaussian Processes with Applications
2. Bayesian Nonparametrics
3. Variational Inference
4. Bayesian Regularization and Variable Selection

The course will run in May 3 - Jun 14 and the lectures (4 hours per topic) will be given on Zoom. More information and schedule on the course page.

The prerequisite for this course is the introductory master level course Bayesian Learning or something equivalent.

Examination: each topic is examined by a computer lab, typically also involving some mathematical derivation. A report is handed in by each student for each topic. There will be deadlines for each topic, but also an extra date after the summer for those who could complete some of the labs during the course.
Course Data
Type of schedule: 
Travel friendly schedule
Credits (ECTS):