SS 18 Bayesian Networks
From Multimedia Computing Lab - University of Augsburg
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Overview
Instructors: | Prof. Dr. Rainer Lienhart, Moritz Einfalt |
Time Lecture: | Tue, 14:00 - 15:30, Room: 2045 N |
Time Exercise: | Tue, 15:45 - 17:15, Room: 2045 N, first exercise on 24.04.2018 |
Credits: | 2 + 2 SWS, LP 5 |
Exam: | See DIGICAMPUS for date and location. (separate registration via STUDIS required) |
Multimedia Teilbereiche: | Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia |
Probability theory is a powerful tool for inferring the value of missing variables given a set of other variables. As the number of variables in a system increases, the joint probability distribution over these variables becomes overwhelmingly large. In this lecture we examine the implications of factoring one large joint probability distribution into a set of smaller conditional distributions and study suitable algorithms for inference.
In order to be admitted to the final exam, students are required to register with the course in DigiCampus and STUDIS. No additional requirements are imposed.
News
- [02.03.2018] Page creation
- [09.04.2018] Date for the first exercise.
- [09.04.2018] Date for the final exam announced (see DIGICAMPUS).
- [20.06.2018] Location for the final exam announced (see DIGICAMPUS).
Literature
MAIN REFERENCE:
- Richard E. Neapolitan. Learning Bayesian Networks. Prentice Hall Series in Artifical Intelligence, 2004. ISBN 0-13-012534-2
ADDITIONAL REFERENCE:
- Daphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press, 2009. ISBN 978-0262013192
Important Comments
- Vorlesung wird auf Deutsch gehalten (trotz der englischsprachigen Folien und Literatur)
- All reading notes are relevant for the exams independent on how thoroughly they have been discussed during the lecture. Thus read and study them carefully.