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CompSci276 Spring 2018, Reasoning in Graphical Models  
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Course Project


Course Reference


Days: Monday/Wednesday Time: 11:00 am  12:20 pm Room: ICS 180 Instructor: Rina Dechter Office hours: Monday 4:00 pm  5:00 pm Course Description The objective of this class is to provide an indepth exposition of representation and reasoning under uncertainty using the framework of Graphical models. The class focuses on reasoning with uncertainty using directed and undirected graphical models such as Bayesian networks, Markov networks and constraint networks. These graphical models encode knowledge as probabilistic relations among variables. The primary reasoning tasks are, given some observations, to find the most likely scenario over a subset of propositions, or to update the degree of belief (distribution) over a subset of the variables. The primary algorithms (exact and approximate) using variational messagepassing, search and sampling will be covered, with illustrations from areas such as bioinformatics, diagnosis and planning. Additional topics may include: causal networks, and dynamic decision networks (Influence diagrams and MDPS), as time permits. Prerequisites
Course material The course will be based mostly on four 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 (70%), class project (30%) 