Lecture Bayesian Networks

Winter Term 2010/2011

Summary

• General Information
• Schedule and Rooms
• Lecturers
• Conditions for Certificates (Scheine) and Exams
• Prerequisites
• Slides
• Assignment Sheets
• Additional Material
• Software
• References
• Links

News

General Information

This page contains information about the lecture "Bayesian Networks" (Bayessche Netze) that is held in winter term 2010/2011 by Prof. Dr. Rudolf Kruse. This page is updated during the course.

Topics

• Modeling of uncertainty and vagueness in expert systems
• Representation of uncertain information in probabilistic networks (Bayesian networks / Markov networks)
• Evidence propagation in such networks
• Quantitative and structural induction of probabilistic networks from data
• Other uncertainty calculi (Dempster-Shafer)
• Applications

Schedule and Rooms

Lecturers

If you have questions regarding the lecture or exercise, please contact (via e-mail if possible) one of the persons named below.

• Prof. Dr. Rudolf Kruse, kruse@iws.cs.uni-magdeburg.de
• Matthias Steinbrecher, msteinbr@iws.cs.uni-magdeburg.de

Conditions for Certificates (Scheine) and Exams

Certificate (Übungsschein): There are assignment sheets published every week. Assignments the solutions of which you want to present in the next exercise lecture have to be ticked beforehand on a votation sheet that is handed our prior to every exercise lecture. If ticked, you may be asked to present your solution in front of class. The solutions need not necessarily be completely correct, however, it should become obvious that you treated the assignment thoroughly. You are granted the certificate (Schein), if (and only if) you

• ticked at least two thirds of the assignments,
• presented at least two times a solution during the exercise, and
• passed a small colloquium (approx. 10 min) or a written test (if there are more than 20 students) after the course.

Exam: If you intend to finish the course with an exam, your are not required to meet the certificate conditions. However, you are of course encouraged to also solve the assignments. Regarding the exam, please contact and negotiate a date and time with Prof. Kruse. The exam has to be announced to the Prüfungsamt two weeks prior to the exam date via this application form. The exam consists of a 20 to 25 minutes oral examination about the subjects presented during the course. Emphasis is put on understanding rather than formal details. The final marks will be in the following range: 1.0, 1.3, 1.7, ..., 3.7, 4.0, 5.0. If more than 20 students intend to do an exam, the oral exam may be converted into a written examination.

Prerequisites

You should have background knowledge on fundamentals of computer science such as algorithms, data structures etc. Also, insights into probability theory are highly recommended.

Slides

Note that the script may be subject to change (which will be stated in the news section above) during the course, i.e. page numbers may change.

• Slides (full)

Assignment Sheets

The assignment sheets will be published weekly at this location.

• Assignment Sheet 1
• Assignment Sheet 2
• Assignment Sheet 3
• Assignment Sheet 4
• Assignment Sheet 5
• Assignment Sheet 6
• Assignment Sheet 7
• Assignment Sheet 8
• Assignment Sheet 9
• Assignment Sheet 10

Additional Material

Feel free to check out the following supplementary material that augment the lecture and exercise.

• Data Mining with Graphical Models dmwgm.pdf
• Kapitel Unsicheres und vages Wissen uvws.pdf
• Illustration of Simple Tree Propagation
• Illustration of Joint Tree Propagation
• Example Network used in the clique propagation lesson.

Software

Here you find links to programs with for learning and using Bayesian networks.

• Induction of Network Structures
• HUGIN Lite (Evidence Propagation)

References

• Graphical Models - Representations for Learning, Reasoning and Data Mining, 2nd Edition.
  C. Borgelt, M. Steinbrecher und R. Kruse.
  J. Wiley & Sons, Chichester, United Kingdom 2009
• Handbuch der künstlichen Intelligenz
  G. Görz, C.-R. Rollinger und J. Schneeberger (Hrsg.).
  Oldenbourg, München, 2000
  In particular: C. Borgelt, H. Timm und R. Kruse.
  Kapitel 9: Unsicheres und vages Wissen.
  uvws.pdf
• An Introduction to Bayesian Networks.
  F.V. Jensen.
  UCL Press, London, United Kingdom 1996
• Expert Systems and Probabilistic Network Models.
  E. Castillo, J.M. Gutierrez, and A.S. Hadi.
  Springer, New York, NY, USA 1997
• Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
  J. Pearl.
  Morgan Kaufmann, San Mateo, CA, USA 1988 (2nd edition 1992)

Links

• Otto-von-Guericke University of Magdeburg
• Faculty of Computer Science
• Department of Knowledge and Language Engineering
• Computational Intelligence Workgroup
• Other Lectures of the Workgroup