Northwestern University 1 credit Statistics
The first half of the course will cover classical reinforcement learning concepts: MDPs, tree search, trajectory optimization, value iteration, policy iteration, and SARSA. The second half will cover modern deep reinforcement learning: deep Q learning, policy gradients, Monte Carlo tree search, inverse reinforcement learning, noise contrastive estimation. We will conclude by discussing philosophical questions related to AI and superhuman intelligence. Prerequisite: Linear algebra (MATH 240-0), some familiarity with Stochastic Gradient Descent, some Python experience, prior experience with PyTorch helpful but not required.
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