One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian belief networks. Intelligent systems based on Bayesian networks are currently being used in a number of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, and data mining.

The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of belief networks.  Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on constructing graphical models and on exact and approximate inference algorithms. Additional topics include learning belief network parameters from Data, dynamic belief networks, reasoning about actions and planning under uncertainty.