This class is usually taught by Gerhard Widmer in the winter term. The class is taught in English.
Information for the current semester (if available):
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Here is a little set of Introduction & Motivation Slides ... , opens an external URL in a new window
This course is a gentle introduction to one of the most important and central classes of methods in present-day Artificial Intelligence. It will introduce students to the basic concepts of Probabilistic Graphical Models as representations of uncertain knowledge in complex domains. All three aspects related to such models will be covered: model semantics, inference, and learning. In particular, the following topics will be covered (in more or less detail):
It is strongly recommended to take this VO together with the "Practical Excercises in Probabilistic Models" (UE, 1h) in the same semester. There, the students will perform practical experiments with some of the methods taught in the VO.
Pdf versions of the Powerpoint slides used in the lecture will be made available via KUSSS (weekly).
Koller, Daphne and Friedman, Nir (2009).
Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press.
Russell, Stuart J. and Norvig, Peter (2003).
Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall.
Questions, suggestions, complaints, etc. to:
Gerhard Widmer, opens an external URL in a new window
Tel. 2468-4701
gerhard dot widmer at jku dot at