This page contains information about the lecture "Bayes Networks" (in German "Bayes-Netze") that is held in winter term 2019/2020 by Prof. Dr. Rudolf Kruse. This page is updated during the course.
No Lecture on October 3rd due to public holiday.
- 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
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.
If you have questions regarding the lecture or exercise, please contact (via e-mail if possible) one of the persons named below.
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.
You should have background knowledge on fundamentals of computer science such as algorithms, data structures etc. Also, insights into probability theory are highly recommended.
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.
|#||Topic||Files||Announcements and Changelog|
|0||Introduction||The lecture will start on October 10th, the first lecture will be held in G29-307|
|5||Probabilistic Graphical Models|
|6||Inference in Belief Trees|
|7||Clique Tree Representations|
|8||Propagation in Clique Trees|
|9||Manual Building of Bayes Networks|
|10||Building Bayes Networks: Parameter Learning|
|11||Learning Decision Trees|
|12||Building Bayes Networks: Structure Learning|
|15||Hidden Markov Models|
The assignment sheets will be published weekly at this location.
Due dates may be adjusted according to the lectures progress.
|ID||Topic||Due Date||Exercise Sheet|
|1||Combinatorics, Certainty Factors, Probabilities||16.10.|
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)
Here you find links to programs with for learning and using Bayesian networks.
- 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
- Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Uffe B. Kjærulff, Anders L. Madsen
Springer Science+Business Media New York 2013