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Instructors: Prof. Dr. rer. nat. Kristian Kersting
Event type:
Integrated Course
Org-unit: Dept. 20 - Computer Science
Displayed in timetable as:
Probabilistic Graphical Models
Subject:
Crediting for:
Hours per week:
4
Language of instruction:
German
Min. | Max. participants:
- | -
Course Contents:
- Refresher of probability & Bayesian decision theory
- Directed and undirected models and their properties
- Inference in tree graphs
- Approximate inference in general graphs: Message passing and mean field
- Learning of directed and undirected models
- Sampling methods for learning and inference
- Modeling in example applications, including topic models
- Deep networks
- Semi-supervised learning
Literature:
Literature recommendations will be updated regularly, an example might be:
- D. Barber: “Bayesian Reasoning and Machine Learning”, Cambridge University Press 2012
- D. Koller, N. Friedman: “Probabilistic Graphical Models: Principles and Techniques”, MIT Press 2009
Preconditions:
Recommended: Participation in “Statistisches Maschinelles Lernen”.
Online Offerings:
moodle
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