Detailed Course Information |
Spring 2024 Semester
May 15, 2024 |
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| CS 5180 - Reinforcement Learning and Sequential Decision Making |
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Introduces reinforcement learning and the underlying computational frameworks and the Markov decision process framework. Covers a variety of reinforcement learning algorithms, including model-based, model-free, value function, policy gradient, actor-critic, and Monte Carlo methods. Examines commonly used representations including deep learning representations and approaches to partially observable problems. Students are expected to have a working knowledge of probability and linear algebra, to complete programming assignments, and to complete a course project that applies some form of reinforcement learning to a problem of interest.
4.000 Credit hours 4.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Lecture Computer Science Department Course Attributes: GSCS Computer & Info Science Restrictions: Must be enrolled in one of the following Programs: MS Data Science PhD Computer Science MS Robotics MSCS Computer Science MSCS Computer Science - Align MS Artificial Intelligence MS Data Science - Align Must be enrolled in one of the following Levels: Undergraduate Graduate Must be enrolled in one of the following Classifications: Graduate |
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