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SS 17 Bayesian Networks

From Multimedia Computing Lab - University of Augsburg


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Overview

Instructors: Prof. Dr. Rainer Lienhart, Christian Eggert
Time Lecture: Tue, 14:00 - 15:30, Room: 2045 N
Time Exercise: Mon, 15:45 - 17:15, Room: 2045 N
Credits: 2 + 2 SWS, LP 5
Exam: Thu, 03.08.2017, 10:00 - 12:00, Room 2045-N
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

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Literature

MAIN REFERENCE:

  1. Richard E. Neapolitan. Learning Bayesian Networks. Prentice Hall Series in Artifical Intelligence, 2004. ISBN 0-13-012534-2

ADDITIONAL REFERENCE:

  1. 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.