Artificial Intelligence

CS 461 frames AI as the problem of getting agents to act rationally under varying amounts of information — from deterministic search where you see everything, to adversarial games, to probabilistic worlds you only glimpse through noisy observations, to environments you have to learn by interacting with. You'll work through the Russell & Norvig canon with quizzes and homeworks for the theory, plus three projects that push you from classical search and Bayes nets into MDPs, reinforcement learning, and a taste of deep RL. It's the synthesis course that ties together your algorithms, probability, and linear algebra background, and it's the natural launchpad into the department's ML, deep learning, and robotics electives.