Probabilistic Machine Learning


Machine Learning can be defined as a set of methods to automatically detect patterns in data which can be used to make predictions in future data. A key concept in this field is uncertainty which arises both through noise measurements and through the finite size of the datasets.

In this course we will adopt a probabilistic approach to machine learning in order to learn under uncertainty. The course is intended for Master and PhD students. Basic knowledge in probability theory and linear algebra would be useful. The main course contents are as follows and are based on the book “Pattern Recognition and Machine Learning” by Christopher M. Bishop.



The lecture takes place every Wednesday from 10-11:30.
The tutorial is scheduled on every Wednesday from 11:30-13:00.

Please register via studip. All lecture materials can be accessed via studip.

  1. Basics of Probability Theory
  2. Bayesian approach to Linear Regression
  3. Bayesian approach to Classification
  4. Neural Networks
  5. Graphical Models
  6. EM and Clustering
  7. Sampling Methods
  8. Variational Inference