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Stanford University

Winter 2023

An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.

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+ 14 more conceptsStanford University

Winter 2023

The course introduces decision making under uncertainty from a computational perspective, covering dynamic programming, reinforcement learning, and more. Prerequisites include basic probability and fluency in a high-level programming language.

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+ 22 more concepts