- 24 hrs lecture
- 4 hrs personal study counseling
- 6 hrs laboratory course
- 20 hrs student presentation
- 26 hrs student project
- 10 hrs problem session
- 78 hrs individual study period
Course "Intelligent Systems"
At the end of this course, the student should be able to:
- understand the principles of reasoning under uncertainty
- understand different numerical models for the representation of uncertainty, such as the CF model, the subjective Bayesian method, Bayesian belief networks, and possibly Dempster-Shafer theory
- have insight into model-based approaches to AI
- have insight into the pros and cons of learning models versus using expert knowledge
- have some experience in experimenting with computational intelligence systems to solve problems involving probability theory
Handling uncertain knowledge has been one of the central problems of AI research during the past 30 years. In the 1970s and 1980s uncertainty was handled by means of formalisms that were linked to rule-based representation and reasoning methods. Since the 1990s probabilistic graphical models, in particular Bayesian networks, are seen as the primary formalisms to deal with uncertain knowledge. Both early and new methods for represensenting uncertainty are studied in the course, where in particular various aspects of Bayesian networks are covered.
- Introduction to Computational Intelligence
- Early models of uncertainty
- Probability theory
- Bayesian networks: principles
- Markov independence
- Reasoning with Bayesian networks
- Building Bayesian networks
- Learning Bayesian networks
Written exam in addition to seminar presentations and practical work.
- P.J.F. Lucas and L.C. van der Gaag, Principles of Expert Systems, Addison-Wesley, Wokingham, 1991, Chapter 5.
- K.B. Korb and A.E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall, Boca Raton, 2004.
- R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J.Spiegelhalter, Probabilistic Networks and Expert Systems,Springer, New York, 1999.
- F.V. Jensen and T. Nielsen, Bayesian Networks and Decision Graphs, Springer, New York, 2007.
The course is part of the AI Masters and also suitable for AI students.