Lectures

This lecture schedule is tentative, and subject to change.

If you would like to see previous term's slides (there will be changes), you can take a look here: Fall 06 or Fall 07

Day Topic Reading Slides Out Due
Tu 1/22 Introduction to AI Python Tutorial 1/31
Th 1/24 Probability Ch 13.1-6
Tu 1/29 Probability Ch 13.1-6
Th 1/31 Probability, Bayes' Nets Ch 14.1-2,4 Homework 1 (p1) 2/7
Tu 2/5 Bayes' Nets Ch 14.3 6PP
Th 2/7 Bayes' Nets Ch 14.4-5
Tu 2/12 Hidden Markov Models Ch 15.1-3 Notes Tutorial Homework 2 (p2) 2/19 in class
Th 2/14 Hidden Markov Models Ch 15.1-3
Tu 2/19 Hidden Markov Models, Speech Recognition Ch 15.6 HMM Programming Project (p3) 2/26 11:59pm
Th 2/21 Speech Recognition, Utility
Tu 2/26
Th 2/28
Utility and Decisions Ch 16.1-3,5
Ch 17.1-3
Utility and Decisions (p4) 3/6 in class
Tu 3/4 Markov Decision Processes, Reinforcement Learning Reinforcement Learning Sutton and Barto Ch 3,4
Th 3/6 Value Iteration, Policy Iteration, Monte Carlo Policy Evaluation Reinforcement Learning
Ch 5, 6.1-5
Tu 3/11 Reinforcement Learning, Classification Ch 20.2 Reinforcement Learning (p5) 3/21 11:59pm
Th 3/13 Perceptrons, Neural Networks Ch 20.5
Tu 3/18
Th 3/20 Midterm
Tu 4/1
Th 4/3
Classification: Neural Networks & Backpropagation, Decision Trees, Nearest Neighbor Ch 18.3
Ch 20.4-5
Tu 4/8 Support Vector Machines Ch 20.6 Neural Networks (p6) 4/17 11:59pm
Th 4/10 A* Search and Heuristics Ch 3.3-4
Ch 4.1-2
Tu 4/15 Mutiplayer Games, Alpha-Beta Pruning Ch 6
Ch 17.6
Th 4/17 Games, Nash equilibrium, Knowledge Representation, Logic Ch 6
Ch 17.6
Notes:
Page 1
Page 2
Homework 7 (p7) 4/24 in class
Tu 4/22 Vision
Th 4/24 Vision Slides
Homework 8 (p8) 5/8 11:59pm
Tu 4/29
Th 5/1
Vision Slides
Tu 5/6
Th 5/8
Language and Review
M 5/19 Final Exam