This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to AIMA unless otherwise specified.
W | Date | Lecture Topic | Readings | Section | Homework | Project |
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1 | M 6/22 | Intro to AI pdf pptx webcast | Ch. 1 & 2 | Worksheet 1 Solutions 1 | HW0 (Due 6/24) HW1 (Due 6/26) Written Assessment 1 (Due 6/29) |
P0: Tutorial (Due 6/26) |
T 6/23 | Uninformed Search pdf pptx webcast | Ch. 3.1–3.4 Note 1 | ||||
W 6/24 | A* Search and Heuristics pdf pptx webcast | Ch. 3.5–3.6 | ||||
Th 6/25 | Game Trees I pdf pptx webcast | Ch. 3.5–3.6, Ch. 16.1–16.3 Note 2 | ||||
2 | M 6/29 | Game Trees II pdf pptx webcast | Ch. 3.5–3.6, Ch. 16.1–16.3 | Worksheet 2 Solutions 2 EP 1 EP Sols 1 Worksheet 3 Solutions 3 EP 2 EP Sols 2 |
HW2 (Due 7/2) |
P1: Search (Due Monday 7/6 11:59 pm) |
T 6/30 | MDP I pdf pptx webcast | Ch. 17.1 - 17.3 Note 3 | ||||
W 7/1 | MDP II pdf pptx webcast | Ch. 17.1 - 17.3 | ||||
Th 7/2 | RL I pdf pptx webcast | Ch. 21; Sutton Ch. 6.1, 6.2, 6.5
Note 4 |
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3 | M 7/6 | RL II pdf pptx webcast | Ch. 21 |
Worksheet 4
Solutions 4
EP 3 EP Sols 3 Midterm Review: Search Midterm Review: Games Midterm Review: MDPs Midterm Review: RL Search Sols Games Sols MDPs Sols RL Sols |
HW3 (Due 7/10) Practice Midterm 1 (Due 7/11 Saturday 11:59 pm) |
P2: Games (Due Friday 7/10 11:59 pm) Mini-Contest 1 (Due 7/16 Thursday 11:59 pm) |
T 7/7 | Probability pdf pptx webcast | Ch. 13.1–13.5 Note 5 | ||||
W 7/8 | BN: Representation pdf pptx webcast | Ch. 14.1, 14.2, 14.4 | ||||
Th 7/9 | BN: Independence pdf pptx webcast | |||||
4 | M 7/13 | Midterm 1 (covers through RL, i.e. Lec 9) | Ch. 14.4 | P3: RL (Due Friday 7/17 11:59 pm) Mini-Contest 2 (Due 8/5 Wednesday 11:59 pm) |
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T 7/14 | BN: Inference | |||||
W 7/15 | BN: Sampling I | |||||
Th 7/16 | BN: Sampling II | Ch. 14.4-14.5 | ||||
5 | M 7/20 | HMM I | Ch. 15.2-15.6 Note 6 |
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T 7/21 | HMM II | Ch. 15.2, 15.6 | ||||
W 7/22 | DN/VPI | Note 7 | ||||
Th 7/23 | ML: Naive Bayes | Ch. 20.1 -20.2.2 Note 8 |
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6 | M 7/27 | TBD: Machine Learning and Review | Ch. 14.4 | P4: Ghostbusters (Due Friday 7/31 11:59 pm) |
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T 7/28 | TBD: Machine Learning and Review | |||||
W 7/29 | Midterm 2 | |||||
Th 7/30 | TBD: Machine Learning and Review | Ch. 18.6.3, 18.8 | ||||
7 | M 8/3 | TBD: Machine Learning and Review | Ch. 16.5 - 16.6 | P5: ML (Due Friday 8/7 11:59 pm) |
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T 8/4 | TBD: Machine Learning and Review | |||||
W 8/5 | TBD: Machine Learning and Review | |||||
Th 8/6 | TBD: Machine Learning and Review | |||||
8 | M 8/10 | Review Lectures and Exam Prep only | |
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T 8/11 | Review Lectures and Exam Prep only | |||||
W 8/12 | Review Lectures and Exam Prep only | |||||
Th 8/13 | Final Exam |