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SS08: Baysian Networks

From Multimedia Computing Lab - University of Augsburg


Registration is open now! LectureReg

Recent Exercise:
Recent Slides:

Overview

Instructors: Prof. Dr. Rainer Lienhart, Eva Hörster
Time Lecture: Thu, 08:15 - 09:45, Room: 207 Eichl.
Time Exercise: Wed, 14:00 - 15:30, Room: 202 Eichl. LectureReg
Credits: 2 + 2 SWS, Schein, yes, LP 5
Exam: Di, 15.07.2008, 8.15 - 9.45, Room: 207 Eichl
Multimedia Teilbereiche: Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia

You may inspect your exams on Wednesday, July 16th, from 10:00-11:30am.

In order to be admitted to the final exam, students are required:

  • to score at least 50% of the points archievable on the weekly assignments
  • to attend the weekly exercise sessions (Wednesday, 14:00-15:30pm). Students are allowed to miss the exercise session at most three times. No exceptions!

In die Klausur darf neben Schreibutensilien, Taschenrechner, und Essen & Getränken nichts mitgenommen werden!.

Literature

  1. Richard E. Neapolitan. Learning Bayesian Networks. Prentice Hall Series in Artifical Intelligence, 2004. ISBN 0-13-012534-2
  2. Martin Schader and Stefan Kuhlins. Programmieren in C++. Springer-Verlag. ISBN 3540637761
    This is a perfect resource for all your questions relating C/C++


Important Comments

  • The exam will be on Juli 15th from 8:15 to 9:45 in Room 207, Eichleitnerstrasse.
  • On July 2nd there will be a lecture instead of an exercise lecture.
  • There will be not exercise lecture on June 11th and no lecture on June 12th.
  • Since May 12th is a holiday, the due date for handing in the solutions to exercise 3 has been moved to Wednesday, May 14th, 9:30 AM.
  • 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

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Date Content Slides Exercise
17.04.2008/24.04.2008 Lec 1/2: Basic of Probability Theory - Part 1 PDF PDF
30.04.2008 PDF
08.05.2008 Lec 3: Basic of Probability Theory - Part 2(a) PDF PDF
Lec 3: Basic of Probability Theory - Part 2(b) PDF
15.05.2008 Lec 4: Bayesian Network based Face Detection PDF PDF
21.05.2008 PDF
29.05.2008 Lec 5: Inference - Part 1 PDF PDF
05.06.2008 Lec 6: Inference - Part 2 PDF PDF
05.06.2008 Lec 7: Inference - Part 3 PDF PDF
05.06.2008 Lec 8: Influence Diagrams PDF PDF
02.07.2008 Lec 9: Parameter Learning - Part 1 PDF PDF
03.07.2008 Lec 10: Parameter Learning - Part 2 PDF
10.07.2008 Lec 11: pLSA PDF