Detailed Course Information |
Spring 2024 Semester
May 15, 2024 |
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| CS 4180 - Reinforcement Learning |
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Introduces reinforcement learning and the Markov decision process (MDP) framework. Covers methods for planning and learning in MDPs such as dynamic programming, model-based methods, and model-free methods. Examines commonly used representations including deep-learning representations. Students are expected to have a working knowledge of probability, 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: Undergraduate Schedule Types: Lecture Computer Science Department Course Attributes: Computer&Info Sci Restrictions: Must be enrolled in one of the following Levels: Undergraduate Prerequisites: Undergraduate level CS 3000 Minimum Grade of D- and (Undergraduate level ECON 2350 Minimum Grade of D- or Undergraduate level ENVR 2500 Minimum Grade of D- or Undergraduate level MATH 3081 Minimum Grade of D- or Undergraduate level PSYC 2320 Minimum Grade of D- or Undergraduate level CS 2810 Minimum Grade of D-) and (Undergraduate level MATH 2331 Minimum Grade of D- or Undergraduate level CS 2810 Minimum Grade of D-) |
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