Probabilistic Graphical Models

• Organized by: Prof. Joschka Boedecker, Shengchao Yan, Hao Zhu
  
• Course number: 11E13MO-1228
• Language: English
• Location: Building 101, SR 01-016/18
• Dates:
• Tuesday 16:15-17:45, lectures, first lecture on 21 April
• Friday 12:15-13:45, exercises and occasionally lectures
• Lecture recordings, slides, exercises and solutions will be posted on ILIAS.

Overview:

The lecture deals with methods of Probabilistic Graphical Models that constitute an important class of machine learning algorithms. A brief content about the topics of the course can be found below. Note that the current course content might change during the semester.

1. Bayesian classifiers
2. Markov models
3. Bayesian networks
4. Inference with Monte Carlo methods
5. Decision graphs
6. Markov decision processes
7. Graphical causal models
8. Deep learning and graphical models

The course materials can also be found here. 

Format: 

The course will be given in person, lectures will be recorded and uploaded to ILIAS.

Exam: 

TBD