Week Date Topics Slides
1 3/2(Tue)  Course Introduction
3/4(Thu)  Lecture 1. Introduction
2 3/9(Tue)  Lecture 2. Intelligent Agents
3/11(Thu)  Lecture 3. Problem Solving by Searching
3 3/16(Tue)  Lecture 4. Beyond Classical Search
3/18(Thu)  Lecture 5. Adversarial Search
4 3/23(Tue)  Lecture 6. Logical Agents
 Project 1 Announcement
3/25(Thu)  Lecture 7. First-Order Logic
5 3/30(Tue)  Lecture 8. Inference in First-Order Logic
4/1(Thu)  Lecture 9. Planning
6 4/6(Tue)  Lecture 10. Uncertainty and Probability
4/8(Thu)  Lecture 11. Bayesian Networks
7 4/13(Tue)  Lecture 12. Probabilitic Reasoning over Time
4/15(Thu)  Review
8 4/20(Tue)  Midterm
4/22(Thu)  Lecture 13. Temporal Probability Models
 Project 2 Announcement
9 4/27(Tue)  Lecture 14. Markov Decision Processes
4/29(Thu)  Lecture 15. Learning from Examples
10 5/4(Tue)  Lecture 16. Deep Neural Networks
5/6(Thu)  Lecture 17. Knowledge in Learning
11 5/11(Tue)  Lecture 18. Learning Probabilistic Models
5/13(Thu)  Lecture 19. Reinforcement Learning
12 5/8(Tue)  Lecture 20. Language
5/20(Thu)  Lecture 21. Vision
13 5/25(Tue)  Lecture 22. Robotics
5/27(Thu)  Lecture 23. Human Level AI
14 6/1(Tue)  Review
6/3(Thu)  Final exam
15
6/8(Tue)  Project 2 Poster Presentation