COMP 440/COMP 557

Artificial Intelligence


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This course is modular in design. All chapter readings and questions below are from the course textbook “Artificial Intelligence: A Modern Approach”, by Russell and Norvig, 3rd edition. All quizzes and assignments are due on Canvas at 8 pm on the dates specified below.


Week 1: Introduction and search (8/21-8/25)

Week 2: Heuristic search and local search (8/28-9/1)

Week 3: Adversarial search and game playing (9/4-9/8)

Week 4: Game theory and MDPs (9/11-9/15)

Week 5: Markov decision processes (9/18-9/22)

Week 6: Constraint satisfaction (9/25-9/29)

Week 7: Bayesian networks (10/2-10/6)

Week 8: BNs and probabilistic reasoning (10/9-10/13)

Week 9: Temporal models and HMMs (10/16-10/20)

Week 10: Decision networks and supervised learning (10/23-10/27)

Week 11: Supervised learning (10/30-11/3)

Week 12: Supervised learning (11/6-11/10)

Week 13: Unsupervised learning (11/13-11/17)

Week 14: Reinforcement learning (11/20-11/24)

Week 15: Reinforcement learning and wrap-up (11/27-12/1)