| CS594 home page | Syllabus | Schedule/Readings | Homework Assignments |
| Date | Topics | Readings | Lecture Notes | HW Assigned | HW Due |
| Thurs. 8/20 | Introduction to AI and Intelligent Agents |
Ch. 1, Ch. 2.1-2.3 | Chapter 1, 2 (part 1) | ||
| Tues. 8/25 | Solving Problems by Searching | Ch. 2.4-2.5, Ch. 3 | Chapter 2 (part 2) Chapter 3 |
HW-1 | Sept. 8 |
| Thurs. 8/27 | Uninformed Search Strategies | Ch. 3 (con't.) | |||
| Tues. 9/01 | Informed Search and Exploration | Ch. 4.1 - 4.3 (through) simulated annealing) |
Chapter 4 | ||
| Thurs. 9/03 | Adversarial Search | Ch. 6 | Chapter 6 | ||
| Tues. 9/08 | Logical Agents | Ch. 7.1 - 7.3 |
Chapter 7 | HW-2 | Sept. 22 |
| Thurs. 9/10 | Logical Agents (con't.) First-Order Logic |
Ch. 7.4 - 7.5 Ch. 8-8.3 Ch. 10.3 (thru pg. 333) |
Chapter 8 |
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| Tues. 9/15 | First-Order Logic and Inference | Ch. 9 | Chapter 9 | ||
| Thurs. 9/17 | First Order Inference (con't) | Ch. 9 | Handout: Exam #1 Study Guide |
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| Tues. 9/22 | Planning | Ch. 11-11.2 | Chapter 11 | HW-3 | Oct. 8 |
| Thurs. 9/24 | Exam #1 (covers Ch. 1-7) | ||||
| Tues. 9/29 | Tour of Distributed Intelligence Lab (no lecture) | ||||
| Thurs. 10/01 | Exams returned (no lecture) | ||||
| Tues. 10/06 | Planning (con't) |
Ch. 11.2-4 | |||
| Thurs. 10/08 | Real-World Planning Uncertainty |
Ch. 12.3-5 Ch. 13 |
Chapter 12 Chapter 13 |
HW-4 | Oct. 20 |
| Tues. 10/13 | Uncertainty (con't.) | Ch. 13 | |||
| Thurs. 10/15 | No Class. Fall Break. | ||||
| Tues. 10/20 | Bayesian Networks |
Ch. 14.1-3 |
Chapter 14a | HW-5 | Oct. 27 (just 1 week!) |
| Thurs. 10/22 | Inference in Bayesian Networks | Ch. 14.4-5 | Chapter 14b Handout: Exam #2 Study Guide |
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| Tues. 10/27 | Temporal Probability Models: Markov processes Filtering and prediction Hidden Markov Models |
Ch. 15.1-2 | Chapter 15 | ||
| Thurs. 10/29 | Exam #2 (covers Ch. 8-14.3) | ||||
| Tues. 11/03 | Temporal Probability Models (con't.): Smoothing Finding most likely sequence Hidden Markov Models |
Ch. 15.2-3 | HW-6 | Nov. 10 (just 1 week!) | |
| Thurs. 11/05 | Temporal Probability Models (con't.) Kalman filters Dynamic Bayesian networks |
Ch. 15.4-5 | |||
| Tues. 11/10 | Temporal Probability Models (con't.) Inference in DBNs Speech Recognition |
Ch. 15.5-6 | HW-7 | Nov. 17 (just 1 week!) | |
| Thurs. 11/12 | Introduction to Learning Statistical Learning Methods Bayesian learning Maximum a posteriori (MAP) learning Maximum likelihood (ML) learning |
Ch. 18.1-2 Ch. 20.1-2 |
Chapter 18.1-2, Chapter 20 | ||
| Tues. 11/17 | Statistical Learning (con't.) ML with discrete or continuous models Naive Bayes Bayesian parameter learning Learning Bayesian network structures |
Ch. 20.2 | HW-8 | Nov. 24 (just 1 week!) | |
| Thurs. 11/19 | Guest lecture by YuanYuan Li (PhD candidate) on detecting time-related anomalies in wireless sensor networks |
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| Tues. 11/24 | Statistical Learning (con't.) EM algorithm |
Ch. 20.3 | HW-9 | Dec. 1 (last one!) | |
| Thurs. 11/26 | No Class. Happy Turkey Day! | ||||
| Tues. 12/01 (Last regular class) |
Statistical Learning (con't.) Course Wrap-up |
Ch. 20 | |||
| Tues. 12/08 12:30-2:30PM |
Exam #3 (during University-scheduled Final Exam period) (covers Ch. 14.4-5, 15, 20) |