Detailed Course Information |
Fall 2021
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. Participants should have taken a graduate-level computer science course and should have some exposure to machine learning from a previous computer-science class or seminar; check with instructor if not sure.
Recommended Prerequisites: CSCI 1950F or CSCI 1420
0.000 OR 1.000 Credit hours 0.000 OR 1.000 Lecture hours 0.000 Lab hours Levels: Graduate, Undergraduate Schedule Types: Discussion Section/Conference, 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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