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September
Monday	Tuesday	Wednesday	Thursday	Friday
28	29	30 09:30-10:20 Lecture SAV 264 Introduction, Agents. RN: Chapter 3, Sections 3.1-3.4. Slides	01	02 09:30-10:20 Lecture SAV 264 Problem Spaces and Blind Search. RN: Chapter 3, Sections 3.5-3.6. Slides

October
Monday	Tuesday	Wednesday	Thursday	Friday
05 09:30-10:20 Lecture SAV 264 Best-first and Uniform-Cost Search RN: Chapter 3, Sections 3.5-3.7 Slides	06	07 09:30-10:20 Lecture SAV 264 A* Search and Heuristics RN: Chapter 4, Sections 4.1-4.2. (continuation of the previous slide set) 23:59 Project 0 (Python tutorial) due	08	09 09:30-10:20 Lecture SAV 264 More on heuristics and search Slides
12 09:30-10:20 Lecture SAV 264 Constraint Satisfaction Problems I RN: Chapter 3, Section 3.7, plus review of pp.104-105 in Chapter 4. Slides	13	14 09:30-10:20 Lecture SAV 264 CSPs, part II	15	16 09:30-10:20 Lecture SAV 264 Adversarial Search, Minimax, Alpha-beta Search RN: Chapter 5, Sections 5.1-5.3 Slides
19 09:30-10:20 Lecture SAV 264 Uncertainty, Expectimax Search RN: Chapter 5, Sections 5.4, 5.5, 5.7, 5.9 Slides 23:59 Project 1 (Search in PacMan) due	20	21 09:30-10:20 Lecture SAV 264 Markov Decision Processes (MDPs) RN: Chapter 17, Sections 17.1, 17.2	22	23 09:30-10:20 Lecture SAV 264 MDPs: Value Iteration RN: Chapter 17, Sections 17.1, 17.2 Slides
26 09:30-10:20 Lecture SAV 264 MDPs: Policy Iteration RN: Chapter 17, Sections 17.2, 17.3 Slides	27	28 09:30-10:20 Lecture SAV 264 Reinforcement Learning RN: Chapter 21, Sections 21.1, 21.2, also Sutton and Barto: Draft 2nd ed. pages 1-144. Slides	29	30 09:30-10:20 Lecture SAV 264 RL Continued (Q-Learning) RN: Chapter 21, Sections 21.3, 21.4 Slides 23:59 Project 2 (Multiagent PacMan) due

November
Monday	Tuesday	Wednesday	Thursday	Friday
02 09:30-10:20 Lecture SAV 264 RL Continued RN: Chapter 21, Sections 21.5	03	04 09:30-10:20 Lecture SAV 264 Review	05	06 09:30-10:20 Midterm
09 09:30-10:20 Lecture SAV 264 Probabilities RN: Chapter 13, Sections 13.1-13.3 Slides	10	11 Veterans Day	12	13 09:30-10:20 Lecture SAV 264 Probabilities (continued)
16 09:30-10:20 Lecture SAV 264 Markov Models RN: Chapter 13, Sections 13.3-13.5, 13.7 Slides	17	18 09:30-10:20 Lecture SAV 264 Hidden Markov Models RN: Chapter 15, Section 15.3 Slides 23:59 Project 3 (Reinforcement Learning) due	19	20 09:30-10:20 Lecture SAV 264 Particle Filters for HMMs RN: Chapter 15, Section 15.5 Slides
23 09:30-10:20 Lecture SAV 264 Bayes Nets RN: Chapter 14, Sections 14.1, 14.2 Slides	24	25 09:30-10:20 Lecture SAV 264 Independence in Bayes Nets RN: Chapter 14, Section 14.2 Slides	26 Thanksgiving Day	27 Day After Thanksgiving
30 09:30-10:20 Lecture SAV 264 Naive Bayes Classification RN: p.499, pp.808-809. Slides	01	02 09:30-10:20 Lecture SAV 264 Perceptrons and Feedforward Neural Nets RN: Chapter 18, Section 18.7 Slides 23:59 Project 4 (Ghostbusters) due	03	04 09:30-10:20 Lecture SAV 264 Classifiers (continued) RN: Chapter 18, Section 18.7 (continued) Slides

December
Monday	Tuesday	Wednesday	Thursday	Friday
07 09:30-10:20 Lecture SAV 264 Computer Vision (presented by Fereshteh Sadeghi) RN: Chapter 23, Sections 24.3-24.5 Slides	08	09 09:30-10:20 Lecture SAV 264 Natural Language Processing (and begin reviewing) Slides 23:59 Optional Competition (PacMan Capture The Flag) due	10	11 09:30-10:20 Lecture SAV 264 Summary and Review 23:59 Project 5 (Pattern Classification) due
14	15	16 08:30-10:20 Final exam	17	18