Mattias Villani

Natural Born Bayesian

Advanced Bayesian Learning

Contents

The course will be divided into the following five topics (responsible teacher in parenthesis):

Intended audience and prerequisites

This course is given to Ph.D. students in Statistics, Computer Science and related subjects. If you are a master or Ph.D. student in another subject and are interested in taking this course, just send me an e-mail and I will get back to you.
The prerequisite for this course is the introductory course Bayesian Learning course (given annually at Statistics, LiU) or something equivalent.

Examination

Individual report on the computer labs, one for each topic.

Schedule
What? Date? Time? Where? Read? Slides/Handouts
Topic 1. Gaussian Processes
Lecturer: Mattias Villani
L1 March, 26 13-15 Von Neumann GitHub repo
Slides
L2 March, 27 10-12 Von Neumann    
Examination/Lab On your own Lab
Data
Topic 2. Bayesian Networks
Lecturer: Jose M. Pena
L1 April 9 10-12 Von Neumann Slides
L2 April 11 10-12 Von Neumann
Examination/Lab On your own  Lab
Topic 3. Approximate Methods
Lecturer: Mattias Villani
L1 April, 23 13-15 Von Neumann GitHub repo
Slides
L2 April, 24 10-12 Von Neumann  
Examination/Lab On your own  Lab
Topic 4. Sequential Monte Carlo
Lecturer: Thomas Schön
L1 May, 8 10-12 Von Neumann Slides
L2 May, 12 10-12 Von Neumann Slides
Examination/Lab On your own  Lab
Data
Topic 5. Bayesian Nonparametrics
Lecturer: Mattias Villani
L1 May, 22 10-12 Von Neumann GitHub repo
Slides
L2 May, 22 15-17 Kurt Gödel  
Examination/Lab On your own  Lab
Data
Copied pages BDA3