Course Description

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 networks, also called graphical models. Intelligent systems based on Bayesian networks are being used in a variety of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, bioinformatics 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 Bayesian networks.  Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on dependency and independency models, on construction Bayesian graphical models and on exact and approximate probabilistic reasoning algorithms. Additional topics include: causal networks, learning Bayesian network parameters from data and dynamic Bayesian networks.

Prerequisites

Course material

The course will be based mostly on three sources:

• Adnan Darwiche, "Modeling and Reasoning with Bayesian Networks"
• Rina Dechter, "Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms"
• Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference"

Additional sources:

• Russell and Norvig, Artificial Intelligence: A Modern Approach
• Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques

A longer list including secondary references.

Some links to software and tools.

Syllabus

Assignments:

Grading Policy: