Lecture Bayesian Networks Winter Term 2016/2017

You can inspect the results of your exam on Monday, 6th of February from 1pm to 3pm in room G29-015.

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 2016/2017 by Prof. Dr. Rudolf Kruse. This page is updated during the course.

Topics

• Representation of uncertain information
• Bayesian networks
• Markov networks
• Evidence propagation in probabilistic networks
• Learning of probabilistic networks
• Revision of probabilistic networks
• Decision Graphs
• Handling imprecise data and imprecise probabilities
• Applications

Schedule and Rooms

Every student who wants to participate in the exercise must register her-/himself via the FIN Registration Service for the exercise. If you have any trouble with verifying the SSL certificate Jens Elkner could help you. While doing the registration, we kindly ask you to give an e-mail address of which incoming e-mail you check regularly.

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
• William Beluch, william.beluch@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
• pass the exam

Exam: If you intend to finish the course with an exam, your are required to meet the certificate conditions. There will be a written exam after the curse. You can use your own not graphical and not programmable calculator.

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 0 - Introduction
• Slides 1 - Uncertainty in Knowledge Based Systems
• Slides 2 - Basics of Applied Probability Theory
• Slides 3 - Decomposible Models
• Slides 4 - Separation Concepts
• Slides 5 - Probabilistic Graphical Models
• Slides 6 - Inference in Belief Trees
• Slides 7 - Inference in Bayes-Networks and Markov-Networks
• Slides 8 - Manual Building of Bayes Networks
• Slides 9 - Building Bayes Networks: Parameter Learning
• Slides 10 - Building Bayes Networks: Structure Learning
• Slides 11 - Belief Change in Probabilistic Graphical Models
• Slides 12 - Decision Graphs / Influence Diagrams
• Slides 13 - Dempster Shafer Theory and Belief Functions

Assignment Sheets

The assignment sheets will be published weekly at this location.

• Assignment Sheet 1 (Deadline 17.10.2016)
• Assignment Sheet 2 (Deadline 24.10.2016)
• Assignment Sheet 3 (Deadline 07.11.2016)
• Assignment Sheet 4 (Deadline 14.11.2016)
• Assignment Sheet 5 (Deadline 21.11.2016)
• Assignment Sheet 6 (Deadline 28.11.2016)
• Assignment Sheet 7 (Deadline 05.12.2016)
• Assignment Sheet 8 (Deadline 12.12.2016)
• Assignment Sheet 9 (Deadline 09.01.2017)
• Assignment Sheet 10 (Deadline 16.01.2017)

Additional Material

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

• Data Mining with Graphical Models dmwgm.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 (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 - A Methodological Introduction
  Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M.
  Springer-Verlag London 2013, 2016
• 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
• 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)
• Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
  Uffe B. Kjærulff, Anders L. Madsen
  Springer Science+Business Media New York 2013

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