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Lecture |
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Reinforcement Learning A2 |
Enrollment Information (not real time - data refreshed nightly)
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Class #:
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24081 | |
Enrollment Capacity:
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80 |
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Section:
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A2 |
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Enrollment Total:
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39 |
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Credits:
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3.00 credits
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Seats Available:
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41 |
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Dates:
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01/30/2023 - 05/12/2023 |
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Status:
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OPEN |
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Days, Time:
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T R , 12:30 PM - 1:50 PM |
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Remote |
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| Location: |
Remote |
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| Reserve Capacities |
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| Description |
Enrollment Capacity |
Enrollment Total |
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| Force Reg: Seats Reserved |
0 |
0 |
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| Eng Sci MS: AI Seats Reserved |
14 |
11 |
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| Data Sciences & Applics MPS |
2 |
1 |
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| Eng Sci MS Data Sci Seats Rsvr |
20 |
7 |
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| Eng Sci MS: Robotics Seats Rsv |
20 |
11 |
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| Enrollment Requirements |
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Prerequisites: Pre/Co Requisite: CSE574 or CSE555 or CSE573 is recommended to be either completed or taken during the same semester |
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| Course Description |
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| This course is intended for students interested in artificial intelligence. Reinforcement learning is an area of machine learning where an agent learns how to behave in an environment by performing actions and assessing the results. Reinforcement learning is how Google DeepMind created the AlphaGo system that beat a high-ranking Go player and how AlphaStar become the first artificially intelligent system to defeat a top professional player in StarCraft II. We will study the fundamentals and practical applications of reinforcement learning and will cover the latest methods used to create agents that can solve a variety of complex tasks, with applications ranging from gaming to finance to robotics. The course is comprised of assignments, short weekly quizzes, a final project and a final exam. |
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| Instructor(s) |
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Vereshchaka |
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| On-line Resources |
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| Other Courses Taught By: Vereshchaka |
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