An Introduction to Probabilistic Machine Learning

Probabilistic machine learning has gained a lot of practical relevance over the past 15 years as it is highly data-efficient, allows practitioners to easily incorporate domain expertise and, due to the recent advances in efficient approximate inference, is highly scalable. Moreover, it has close relations to causal inference which is one of the key methods for measuring cause-effect relationship of machine learning models and explainable artificial intelligence. This openHPI course will introduce all recent developments in probabilistic modeling and inference. It will cover both the theoretical as well as practical and computational aspects of probabilistic machine learning.

This course requires Julia programming; we will use the CodeOcean feature of openHPI. We will also assume that the participants have a solid understanding of analysis and calculus.

LITERATURE

In this course, we will make use of the following four textbooks:

1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
   
2. Koller, D., and N. Friedman. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
   
3. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
   
4. Ross, S. (2021). Introduction to Probability and Statistics for Engineers and Scientists. Academic Press.