This course covers a broad range of AI-related topics at a fast pace including machine learning, search algorithms, Markov decision process, games, constraint satisfaction problem, graphical models, and logic.
This course focuses on understanding the fundamental concepts and principles on each topic.
Accordingly, programming assignments will be implementing the core ideas with native python rather than blindly using AI libraries or tools.
Name: Hwanjo Yu
Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks.
Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning. (Available free online)
Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers machine learning. (Available free online)
Tsang. Foundations of constraint satisfaction. Covers constraint satisfaction problems. (Available free online)
W1. AI Introduction (HW1 out)
W2. ML1: Linear predictor, Loss minimization, Stochastic Gradient Descent (SGD)
W3. ML2 & 3: Features, Neural network, kNN, Generalization, Unsupervised learning (HW2 out)
W4. Search1: Tree search, Dynamic programming, Uniform cost search
W5. Search2: Learning costs, A* (HW3 out)
W6. MDP1: Markov Decision Process
W7. MDP2: Reinforcement Learning (HW4 out)
W8. Midterm
W9. Games1&2 (HW5 out)
W10. CSP1
W11. CSP2 (HW6 out)
W12. Bayes1
W13. Bayes2&3 (HW7 out)
W14. Logics
W15. Conclusion (HW8 out.. Actually we did not learn Logics, also do HW8)
W16. Final exam