Lectures

This lecture schedule is subject to change. In particular, the midterm date will not be finalized until a week or so into the course.
You may want to look at last term's slides, but there will be changes.
Note: Unreleased project out and due dates are just guesses and might change slightly.
Webcasts available at: Audio/Video on iTunes, Audio on iTunes, Audio/Video on webcast.berkeley.edu

Day Topic Reading Slides Out Due
Tu 1/17 Introduction to AI Ch. 1 2PP 6PP
Th 1/19 Agents and Search Ch. 3.1-4 (2e: 3) 2PP 6PP P0: Python Tutorial 1/27
Tu 1/24 A* Search and Heuristics Ch. 3.5-6 (2e: 4.1-2) see previous lecture P1: Search 2/3
Th 1/26 Constraint Satisfaction Problems Ch. 6.1 (2e: 5.1) 2PP 6PP
Tu 1/31 CSPs II Ch. 6.2-5 (2e: 5.2-4) see previous lecture  
Th 2/2 Game Trees: Minimax, Alpha-Beta Ch. 5.2-5 (2e: 6.2-5) 2PP 6PP
Tu 2/7 Game Trees: Expectimax; Utility Theory Ch. 5.2-5 (2e: 6.2-5) 2PP 6PP P2: Multi-Agent Pacman 2/17
Th 2/9 Markov Decision Processes Sutton and Barto Ch. 3-4 2PP 6PP
Tu 2/14 MDPs II Ch. 17.1-3 (2e: 17.1-3), Sutton and Barto Ch. 6.1,2,5 see previous lecture
Th 2/16 Reinforcement Learning   2PP 6PP
Tu 2/21 Midterm I Review Lecture   2PP 6PP practice midterm1
Th 2/23 Midterm I LOCATION: 155 Dwinelle
Tu 2/28 Reinforcement Learning II see previous lecture on RL P3: Reinforcement Learning 3/12
Th 3/1 Probability Ch. 13.1-5 (2e: 13.1-6) 2PP 6PP
Tu 3/6 Bayes' Nets: Representation Ch. 14.1-2,4 (2e: 14.1-2,4) 2PP 6PP    
Th 3/8 Bayes' Nets: Independence Ch. 14.3 (2e: 14.3), Jordan 2.1 see previous lecture
Tu 3/13 Bayes' Nets: Inference Ch. 14.4-5 (2e: 14.4-5) 2PP 6PP
Th 3/15 Bayes' Nets: Sampling Ch. 14.4-5 (2e: 14.4-5) see previous lecture
Tu 3/20 Midterm II Review Lecture 2PP 6PP
Th 3/22 Midterm II LOCATION: 2050 Valley LSB
Tu 3/27

Spring Break
Th 3/29

Spring Break
Tu 4/3 HMMs, DBNs, Particle Filtering, Viterbi Ch. 15.2,5-6 (2e: 15.2,5-6) 2PP 6PP P4: Ghostbusters 4/17
Th 4/5 Speech Ch. 15.1-3,6 (2e: 15.1-3,6) 2PP 6PP Contest: Pacman Capture The Flag 4/25 [Wednesday!]
Tu 4/10 Decision Diagrams, VPI Ch. 16.5-6 2PP 6PP
Th 4/12 ML: Naive Bayes Ch. 20.1, 20.2.1, 20.2.2 2PP 6PP
Tu 4/17 ML: Perceptron and Optimization   2PP 6PP P5: Classification 4/30
Th 4/19 ML: Perceptron and Optimization (ctd) see previous lecture
Tu 4/24 Applications: Robotics [not study material for final] 2PP 6PP
Th 4/26 Applications: Computer Vision, Language; Final Contest and Conclusion [not study material for final] 2PP 6PP 2PP 6PP
Tu 5/1 Final Review Session 1: Search, CSPs, Game Trees, Utility, MDPs, RL 2PP 6PP 2PP 6PP
Th 5/3 Final Review Session 2: Probability, Bayes' Nets, HMMs, DBNs, Particle Filtering, Viterbi, Decision Diagrams, VPI, ML 2PP 6PP 2PP 6PP
F 5/11

Final Exam 11:30-2:30pm, 1 Pimentel