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CSE 473 - Introduction to Artificial Intelligence - Spring 2014 Mon, Wed, Fri 1:30-2:20 in MGH 241 |
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| Instructor:
Hanna Hajishirzi
(hannaneh at cs dot washington dot edu) Office hours: Tuesday 3-3:40pm at EEB214 Course staff mailing list: (cse473-staff at cs dot washington dot edu) |
TA:
Svetoslav Kolev
(swetko at cs dot washington dot edu) Office hours: Wednesday 3:00-4:00pm at CSE002 |
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TA: Yunyi Song
(bessieyy at cs dot washington dot edu) Office hours: Thursday 10:30-11:30am at CSE002 |
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TA: Johnson Goh
(johnson at cs dot washington dot edu) Office hours: Monday 10:30-11:30am at CSE002 |
| Week | Dates | Topics & Lecture Notes | Readings |
|---|---|---|---|
| 1 | March 31 | Introduction, Agents | R&N, Ch. 1,2 (optional) |
| 2 | April 2 | Agents, Problem Spaces and Blind Search | R&N, Ch. 3.1-3.4 |
| 3 | April 4 | Search algorithms: Cost and Heuristics | R&N, Ch. 3.5-3.7 |
| 4 | April 7 | Search Algorithms: A* | R&N, Ch. 3.5-3.7 |
| 5 | April 9 | Adversarial Search (Minimax) | R&N, Ch. 5.1-5.2 |
| 6 | April 11 | Adversarial Search (Alpha-Beta) | R&N, Ch. 5.3-5.4 |
| 7 | April 14 | Adversarial Search (Expectimax) | R&N, Ch. 5.5-5.7 (5.6 is optional) |
| 8 | April 16 | Markov Decision Processes | R&N, Ch. 17.1-17.3, S&B, Ch. 3-4 |
| 9 | April 18 | Markov Decision Processes | R&N, Ch. 17.1-17.3, S&B, Ch. 3-4 |
| 10 | April 21 | MDP(ctd.), Reinforcement Learning | R&N, Ch. 21.1-21.2 (also, finish previous reading) |
| 11 | April 23 | Reinforcement Learning | R&N, Ch. 21.3 |
| 12 | April 25 | Reinforcement Learning | R&N, Ch. 21.3 |
| 13 | April 28 | Uncertainty | R&N, Ch. 13 |
| 14 | April 30 | Probablistic Inference | R&N, Ch. 13 |
| 15 | May 2 | Probabilistic Inference (ctd.), Markov Models | R&N, Ch. 15.1 (also finish previous reading) |
| 16 | May 5 | Hidden Markov Models | R&N, Ch. 15.1-15.3 |
| 17 | May 7 | Hidden Markov Models (Forward Algorithm) | R&N, Ch. 15.1-15.3 |
| 18 | May 9 | Hidden Markov Models (Particle Filtering) | R&N, Ch. 15.1-15.3 |
| 19 | May 12 | Applications: Robotics | |
| 20 | May 14 | Bayesian Networks | R&N, Ch. 14.1-14.5 |
| 21 | May 19 | Bayesian Networks (Independence) | R&N, Ch. 14.1-14.5 |
| 22 | May 21 | Bayesian Networks (Inference) | R&N, Ch. 14.1-14.5 |
| 23 | May 23 | Bayesian Networks (Sampling) | R&N, Ch. 14.1-14.5 |
| 24 | May 26 | Machine Learning (Naive Bayes) | R&N, Ch. 18.1-2 |
| 25 | May 28 | Machine Learning (Perceptron) | R&N, Ch. 18.4-6 |
| 26 | June 4 | Logic | R&N, Ch. 6 |
| 27 | June 6 | Logic, Applications | R&N, Ch. 6 |
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Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX |