Lecture Bayesian Networks Winter Term 2014/2015

• The exam is now revised. You can have a look at your exam on friday 13th of march 2015 between 9a.m. and 11a.m. in room G29-036 - the final grades will be determined afterwards.

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" (in German "Bayes-Netze") that is held in winter term 2014/2015 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
• Pascal Held, pheld@ovgu.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 required to meet the certificate conditions.

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 0a - Organizational Stuff
• Slides 0 - Introduction
• Slides 1 - Rule-based Systems
• Slides 2 - Elements of Graph Theory
• Slides 3 - Decomposition
• Slides 4 - Probability Foundations
• Slides 5 - Applied Probability Theory
• Slides 6 - Probabilistic Graphical Models
• Slides 7 - Propagation in Belief Networks
• Slides 8 - Learning Graphical Models (updated 2015-02-17)
• Slides 9 - Decision Graphs / Influence Diagrams
• Slides 10 - Nonstandard Frameworks of Imprecision and Uncertainty

Assignment Sheets

The assignment sheets will be published weekly at this location.

• Assignment Sheet 1 (Deadline 20.10.2014)
• Assignment Sheet 2 (Deadline 27.10.2014)
• Assignment Sheet 3 (Deadline 03.11.2014)
• Assignment Sheet 4 (Deadline 10.11.2014)
• Assignment Sheet 5 (Deadline 24.11.2014)
• Assignment Sheet 6 (Deadline 15.12.2014)
• Assignment Sheet 7 (Deadline 12.01.2015)
• Assignment Sheet 8 (Deadline 19.01.2015)
• Assignment Sheet 9 (Deadline 26.01.2015)

In this year we want to setup a mailing list. This list should give you the opportunity to exchange interesting solutions and also solve problems concerning the topic. Additionally I will send important information and the current Assignment Sheet via the list. If you like to join this list, please send an email to pheld@ovgu.de .

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.
• Blood group determination of Danish Jersey cattle in the F-blood group system
• Introductory slides about belief functions Δ (Source: Thierry Denoeux)

Software

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

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

References

• Computational Intelligence
  R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, G. Ruß, M. Steinbrecher.
  Vieweg+Teubner, Wiesbaden, Germany 2011
• 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