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CompSci276 Spring 2017, Reasoning in Probabilistic Graphical Models: Belief Networks  
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Days: Monday/Wednesday Time: 11:00 am  12:20 pm Room: DBH 1427 Instructor: Rina Dechter Office hours: Monday 3:00 pm  4:00 pm 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 realworld applications including diagnosis, sensor fusion, online help systems, credit assessment, bioinformatics and data mining. The objective of this class is to provide an indepth 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:
Additional sources:
A longer list including secondary references. Some links to software and tools. Syllabus
Assignments: There will be homework assignments and students will also be engaged in projects. Grading Policy: Homework and exam (75%), class project (25%) 