Introduction to Probabilistic Machine Learning (ST 2023)

Prof. Dr. Ralf Herbrich

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 relationships of machine learning models and explainable artificial intelligence. This 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. In the course, we will implement all the inference techniques and apply them to real-world problems.



Date: April 17, 2023
Language: English
Duration: 01:29:27

Information & Inference

Date: April 24, 2023
Language: German
Duration: 01:35:56

Information & Inference (2)

Date: May 8, 2023
Language: English
Duration: 01:31:03

Linear Basic Function Models

Date: May 15, 2023
Language: German
Duration: 01:29:33

Bayesian Regression

Date: May 22, 2023
Language: English
Duration: 01:27:02