Graphical Probability Models. Bayesian Reasoning. Bayesian Networks. Learning in Bayesian Networks. Knowledge Engineering. Temporal Models. Inference in Dynamic Bayesian Networks. Markov Decision Processes.

Course Objectives

The challenges of modeling and reasoning under uncertainty is defined and solution approaches based on probabilistic reasoning are discussed in this course.

Recommended or Required Reading

Bayesian Artificial Intelligence; Kevin B. Korb, Ann E. Nicholson; Chapman & Hall/CRC, 2004. ,Learning Bayesian Networks; Richard E. Neapolitan, Prentice Hall, 2003. ,Probabilistic Reasoning in Intelligent Systems (2nd Edition); Judea Pearl; Morgan Kaufmann Publishers Inc., 1988.

Learning Outcomes

1. Teaching the concept of uncertainty

2. To teach graphical probabilistic models

3. Being able to understand reasoning and learning in Bayesian networks

4. Being able to design Bayesian network applications

Grading

Midterm : 15%

Homework : 20%

Research Presentation: 30%

Final : 35%