Detailed Course Information |
Fall 2018
Apr 19, 2024 |
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| CSCI 2951F - Learning and Sequential Decision Making |
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The course explores automated decision making from a computer-science perspective. It examines efficient algorithms, where they exist, for single agent and multiagent planning as well as approaches to learning near-optimal decisions from experience. Topics will include Markov decision processes, stochastic and repeated games, partially observable Markov decision processes, and reinforcement learning. Of particular interest will be issues of generalization, exploration, and representation. Each student will be expected to present a published research paper and will participate in a group programming project. Prerequisite: a graduate-level computer science course and some exposure to reinforcement learning from a previous computer-science class or seminar.
1.000 Credit hours 1.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Primary Meeting Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Graduate Prerequisites: Graduate level CSCI 1950F Minimum Grade of S or Graduate level CSCI 1420 Minimum Grade of S |
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