COMPSCI 4453A
0.50 credit · Main
This course explores fundamental concepts, algorithms, and applications in reinforcement learning, bridging AI and machine learning. Through lectures and practical exercises, students study Markov Decision Processes, Bellman equations, and key algorithms such as Q-learning, and DQN. They discover Reinforcement Learning’s road applicability in robotics, gaming, finance, and healthcare domains.
Data Science 3000A/B.
3 lecture hours.
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