Lectures on Bayesian Machine Learning
Lecturer: Mattias Villani
Positions: Professor of Statistics at Stockholm and Linköping University.
Research: Computationally efficient Bayesian methods for inference, prediction and decision making with flexible probabilistic models.
Teaching: Bayesian Learning, Introduction to Machine Learning, Advanced Machine Learning, Text Mining, Machine Learning for Industry, Probability and Statistics for Machine Learning etc
Current Application Areas: Transportation, Neuroimaging, Robotics, Econometrics and Software Development.
Web: Web page
Google Scholar: Profile
Genealogy: Mathematics Genealogy Project
Lecture 1 - The Bayesics
Lecture 2 - Gaussian Process Regression
Reading: Slides | Chapter 2 and 4-5 in Gaussian Processes for Machine Learning.
Code: GP for LIDAR data | demo hyperparameter optimization Python
Software: GP fit package R | GPStuff for Matlab | GPy for Python