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Lecture

Section

Lab

Exam

Homework

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Other

Holiday

September
Monday	Tuesday	Wednesday	Thursday	Friday
24	25	26 14:30-15:20 Lecture SIG 134 Introduction, Agents. 1.1, 2.1-2.4. Slides 1-up	27	28 14:30-15:20 Lecture SIG 134 Problem Spaces and Blind Search. 3.1-3.4. Slides 1-up

October
Monday	Tuesday	Wednesday	Thursday	Friday
01 14:30-15:20 Lecture SIG 134 Heuristic Search 3.5-3.6. Slides 1-up	02	03 14:30-15:20 Lecture SIG 134 More on Heuristics Slides 1-up	04	05 14:30-15:20 Lecture SIG 134 Heuristics (cont). 23:59 Project 0 (Python tutorial) due
08 14:30-15:20 Lecture SIG 134 Constraint Satisfaction Problems, Part 1. 6.1-6.3. Slides 1-up	09	10 14:30-15:20 Lecture SIG 134 Constraint Satisfaction Problems, Part 2. 6.4-6.5. Slides 1-up	11	12 14:30-15:20 Lecture SIG 134 Adversarial Search, Minimax, Alpha-beta Search. 5.1-5.3 Slides 1-up
15 14:30-15:20 Lecture SIG 134 Adversarial Search (continued). 23:59 Project 1 (Search in PacMan) due	16	17 14:30-15:20 Lecture SIG 134 Uncertainty, Expectimax Search. 5.4-5.5. Slides 1-up	18	19 14:30-15:20 Lecture SIG 134 Markov Decision Processes (MDPs). 17.1. Slides 1-up
22 14:30-15:20 Lecture SIG 134 Markov Decision Processes (continued).	23	24 14:30-15:20 Lecture SIG 134 MDPs: Value Iteration. 17.2.	25	26 14:30-15:20 Lecture SIG 134 MDPs: Policy Iteration. 17.3 Slides 1-up 23:59 Project 2 (Multiagent PacMan) due
29 14:30-15:20 Lecture SIG 134 Reinforcement Learning. 21.1-21.2; also Sutton and Barto: Draft 2nd ed. pages 1-144. Slides 1-up	30	31 14:30-15:20 Lecture SIG 134 Reinforcement Learning (cont). 21.3, 21.4	01	02 14:30-15:20 Midterm exam

November
Monday	Tuesday	Wednesday	Thursday	Friday
05 14:30-15:20 Lecture SIG 134 Reinforcement Learning (cont)	06	07 14:30-15:20 Lecture SIG 134 Reinforcement Learning (cont) -- feature-based approximate Q-Learning. Slides 1-up	08	09 14:30-15:20 Lecture SIG 134 Probability Review. 13.1-13.3 Slides 1-up 23:59 Project 3 (Reinforcement Learning) due
12 Veteran's Day*	13	14 14:30-15:20 Lecture SIG 134 Markov Models. RN: Chapter 13, Sections 13.4-13.5. Slides 1-up	15	16 14:30-15:20 Lecture SIG 134 Hidden Markov Models. 15.3 Slides 1-up
19 14:30-15:20 Lecture SIG 134 Particle Filters for HMMs. 15.5	20	21 14:30-15:20 Lecture SIG 134 Perceptrons. Slides 1-up 23:59 Project 4 (Ghostbusters) due	22 Thanksgiving	23 Fri. after Thanksgiving
26 14:30-15:20 Lecture SIG 134 Bayes Nets Slides 1-up	27	28 14:30-15:20 Lecture SIG 134 D-Separation in Bayes Nets; [NOTE: The P5 deadline is moved to Thursday, Nov. 29.] Slides 1-up 23:59 Project 5 (Pattern Classification) due	29	30 14:30-15:20 Lecture SIG 134 (a) Robotics Laws; (b) Multilevel Perceptrons (a) Slides 1-up (b) Slides 1-up ,(c) optional reading on Asimov Laws.

December
Monday	Tuesday	Wednesday	Thursday	Friday
03 14:30-15:20 Lecture SIG 134 Advanced Applications: NLP 22.1-22.4. Slides 1-up	04	05 14:30-15:20 Lecture SIG 134 Review 23:59 Homework 6 (Written Exercises) due	06	07 14:30-15:20 Lecture SIG 134 Course wrapup.
10	11 14:30-16:20 Final exam	12	13	14