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SS 10: Bayesian Networks

From Multimedia Computing Lab - University of Augsburg


Registration is open now! LectureReg

Overview

Instructors: Prof. Dr. Rainer Lienhart, Fabian Richter
Time Lecture: Mon, 12:15 - 13:45, Room: 1058 N
Time Exercise: Mon, 10:00 - 11:30, Room: 1058 N LectureReg
Start May, 10th 2010
Credits: 2 + 2 SWS, Schein, yes, LP 5
Examine: Fri, 10:00 - 11:30, Room: 1058 N + 1055 N, August, 6th 2010
Multimedia Teilbereiche: Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia


In order to be admitted to the final exam, students are required to register with the course in LectureReg. No additional requirements are imposed.

News

  • The lecture on Monday, June 14th, is postponed by one week. The 5th assignment will be discussed on June 21st.
  • Exam results are now online in LectureReg. You may inspect your exams on Monday, August 23rd, from 2:00-3:00pm.

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

USEFULL C++ REFERENCES:

  1. Simon Hoffmann and Rainer Lienhart. OpenMP: Eine Einf├╝hrung in die parallele Programmierung mit C/C++', Springer Verlag, April 2008, ISBN: 978-3-540-73122-1. (zur Verlagsseite)


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.

Material

Date Content Slides Exercise
19.04.2010 Lec 1: Introduction PDF
26.04.2010
03.05.2010
Lec 2: Basic of Probability Theory - Part 1 PDF PDF
10.05.2010 Lec 3: Basic of Probability Theory - Part 2(a) PDF PDF, Solution
17./31.05.2010 Lec 4: Basic of Probability Theory - Part 2(b) PDF PDF
31.05.2010 Lec 5: Bayesian Network based Face Detection PDF PDF
PDF
07.06.2010 Lec 5: Inference - Part 1 PDF PDF
22.06.2010 Lec 6: Inference - Part 2 PDF PDF
29.06.2010 Lec 7: Inference - Part 3 PDF PDF
05.07.2010 Lec 8: Influence Diagrams PDF PDF
12.07.2010 Lec 9: Parameter Learning - Part 1 PDF
12./19. 07.2010 Lec 10: Parameter Learning - Part 2 PDF
19.07.2010 Fragestunde