This lecture schedule is tentative, and subject to change.

If you would like to see last term's slides (there will be substantial changes), you can take a look here.

Day Topic Reading Slides Out Due
Tu 8/29 Introduction to AI Ch. 1, 2 (both general) 2PP  6PP Mazeworld 9/8
Th 8/31 Agents and Search Ch. 3 2PP  6PP    
Tu 9/5 A* Search and Heuristics Ch. 4.1-2 2PP  6PP    
Th 9/7 CSPs I Ch. 5.1 2PP  6PP Pacman: Search and Agents 9/19
Tu 9/12 CSPs II Ch. 5.2-4 2PP  6PP    
Th 9/14 Robot Motion (demo) Ch. 25.4 2PP  6PP    
Tu 9/19 Game Trees: Minimax Ch. 6.2-5 2PP  6PP Pacman: Adversarial 9/29
Th 9/21 Game Trees: Expectimax Approximation Ch. 6.2-5 2PP  6PP    
Tu 9/26 Markov Decision Processes Sutton and Barto Ch. 3-4 2PP  6PP    
Th 9/28 MDPs II Ch. 17.1-3, Sutton and Barto Ch. 6.1,2,5 2PP  6PP RL: GridWorld / Crawler 10/16
Tu 10/3 Reinforcement Learning (as before) 2PP  6PP    
Th 10/5 Utilities and Review   2PP  6PP    
Tu 10/10 MIDTERM EXAM (in class)        
Th 10/12 Reinforcement Learning II   2PP  6PP    
Tu 10/17 Probability and Belief Ch. 13.1-6 2PP  6PP Battleship: Probabilities 10/23
Th 10/19 Bayes' Nets I Ch. 14.1-2,4 2PP  6PP  
Tu 10/24 Bayes' Nets II Ch. 14.3, Jordan 2.1 2PP  6PP  
Th 10/26 Bayes' Nets III Ch. 14.4-5 2PP  6PP  
Tu 10/31 Decision Diagrams Ch. 16.5-6 2PP  6PP Battleship: Inference 11/9
Th 11/2 Hidden Markov Models Ch. 15.1-3,6 2PP  6PP    
Tu 11/7 Tracking and Particle Filtering Ch. 15.2,5 2PP  6PP Battleship: Static 11/15
Th 11/9 Speech Recognition and Viterbi Ch. 15.2,6 2PP  6PP  
Tu 11/14 ML: Naive Bayes   2PP  6PP Battleship: Dynamic 11/27
Th 11/16 Estimation and Review   2PP  6PP  
Tu 11/21 MIDTERM EXAM (optional, in class)        
Th 11/23 No Class
Tu 11/28 ML: Perceptrons   2PP  6PP Classification 12/6
Th 11/30 ML: Clustering   2PP  6PP    
Tu 12/5 Vision   Slides       
Th 12/7 Advanced Topics and Pacman Contest