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Lecture

Section

Lab

Exam

Homework

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Holiday

March
Monday	Tuesday	Wednesday	Thursday	Friday
26 14:30-15:20 Lecture MGH 241 Introduction, Agents. RN: Chapter 3, Sections 3.1-3.4. Slides 1-up	27	28 14:30-15:20 Lecture MGH 241 Problem Spaces and Blind Search. RN: Chapter 3, Sections 3.5-3.6. Slides 1-up	29	30 14:30-15:20 Lecture MGH 241 Heuristic Search RN: Chapter 3, Sections 3.5-3.7 Slides 1-up

April
Monday	Tuesday	Wednesday	Thursday	Friday
02 14:30-15:20 Lecture MGH 241 More on Heuristics RN: Chapter 4, Sections 4.1-4.2. Slides 1-up	03	04 14:30-15:20 Lecture MGH 241 Heuristics (cont) 23:59 Project 0 (Python tutorial) due	05	06 14:30-15:20 Lecture MGH 241 Constraint Satisfaction Problems, Part 1 RN: Chapter 3, Section 3.7, plus review of pp.104-105 in Chapter 4. Slides 1-up
09 14:30-15:20 Lecture MGH 241 Constraint Satisfaction Problems, Part 2 Slides 1-up	10	11 14:30-15:20 Lecture MGH 241 Adversarial Search, Minimax, Alpha-beta Search RN: Chapter 5, Sections 5.1-5.3 Slides 1-up	12	13 14:30-15:20 Lecture MGH 241 Adversarial Search (continued) 23:59 Project 1 (Search in PacMan) due
16 14:30-15:20 Lecture MGH 241 Uncertainty, Expectimax Search RN: Chapter 5, Sections 5.4, 5.5, 5.7, 5.9 Slides 1-up	17	18 14:30-15:20 Lecture MGH 241 Markov Decision Processes (MDPs) RN: Chapter 17, Sections 17.1, 17.2 Slides 1-up	19	20 14:30-15:20 Lecture MGH 241 Markov Decision Processes (continued)
23 14:30-15:20 Lecture MGH 241 MDPs: Value Iteration RN: Chapter 17, Sections 17.1, 17.2 23:59 Project 2 (Multiagent PacMan) due	24	25 14:30-15:20 Lecture MGH 241 MDPs: Policy Iteration RN: Chapter 17, Sections 17.2, 17.3 Slides 1-up	26	27 14:30-15:20 Lecture MGH 241 Reinforcement Learning RN: Chapter 21, Sections 21.1, 21.2, also Sutton and Barto: Draft 2nd ed. pages 1-144. Slides 1-up
30 14:30-15:20 Midterm exam	01	02 14:30-15:20 Lecture MGH 241 Reinforcement Learning (cont) RN: Chapter 21, Sections 21.3, 21.4 Slides 1-up	03	04 14:30-15:20 Lecture MGH 241 Reinforcement Learning (cont) -- feature-based approximate Q-Learning

May
Monday	Tuesday	Wednesday	Thursday	Friday
07 14:30-15:20 Lecture MGH 241 Probability Review RN: Chapter 13, Sections 13.1-13.3 Slides 1-up 23:59 Project 3 (Reinforcement Learning) due	08	09 14:30-15:20 Lecture MGH 241 Markov Models RN: Chapter 13, Sections 13.3-13.5, 13.7 Slides 1-up	10	11 14:30-15:20 Lecture MGH 241 Hidden Markov Models RN: Chapter 15, Section 15.3 Slides 1-up
14 14:30-15:20 Lecture MGH 241 Particle Filters for HMMs. (Optional TOH Values Iteration program due at 11:59 PM). RN: Chapter 15, Section 15.5	15	16 14:30-15:20 Lecture MGH 241 HMMs (cont.)	17	18 14:30-15:20 Lecture MGH 241 Perceptrons. Slides 1-up 23:59 Project 4 (Ghostbusters) due
21 14:30-15:20 Lecture MGH 241 Bayes Nets Slides 1-up	22	23 14:30-15:20 Lecture MGH 241 D-Separation in Bayes Nets Slides 1-up	24	25 14:30-15:20 Lecture MGH 241 Advanced Applications: NLP RN: Chapter 22, pp.860-887. Slides 1-up 23:59 Project 5 (Pattern Classification) due
28 Memorial Day	29	30 14:30-15:20 Lecture MGH 241 Bayes Net exact inference using Variable Elimination RN: Chapter 14, Section 14.4. Slides 1-up	31	01 14:30-15:20 Lecture MGH 241 Social Issues and the Future of AI, incl. Asimov's 3 Laws of Robotics; course wrapup. Slides (Future of AI) 1-up Slides (Course wrapup) 1-up 23:59 Homework 6 (Written Exercises) due

June
Monday	Tuesday	Wednesday	Thursday	Friday
04	05 14:30-16:20 Final exam	06	07	08