Date |
Content |
Readings |
| Tu 9/22 | Lecture 1: Logistics, History of AI, Agents | 1.2, 25.1, 25.2, 25.8 |
| ROBOTICS & SEARCH | ||
| Th 9/24 | Lecture 2: Robotics, Motion planning, Basic search | 25.4, 3.1-3.5, 3.7 |
| Tu 9/29 | Lecture 3: Heuristic search: A* | 4.1-4.2 |
| Th 10/1 | Lecture 4: Optimization search | 4.2-4.4, 4.6 |
| Tu 10/6 | Lecture 5: CSPs | 5.1-5.3 |
| MACHINE LEARNING | ||
| Th 10/8 | Lecture 6: Supervised learning, Linear classifiers | 18.1, 18.2, 20.5 |
| Tu 10/13 | Lecture 7: Decision trees | 18.3, 18.5, 18.6 |
| Th 10/15 | Lecture 8: Reinforcement learning | 17.1-17.2, 21.1-21.4 |
| COMPUTER VISION | ||
| Tu 10/20 | Lecture 9: Vision: Features & Object recognition | 24.1, 24.3, 24.5 |
| Th 10/22 | Lecture 10: Vision: Geometry | 24.2, 24.4 |
| PROBABILISTIC REASONING | ||
| Tu 10/27 | Lecture 11: Bayesian networks | 14.1, 14.2 |
| Th 10/29 | Lecture 12: Inference in Bayesian networks | 14.4 |
| Tu 11/3 | Lecture 13: Learning in Bayesian networks | 20.2, 20.3 |
| Th 11/5 | Lecture 14: Undirected probabilistic models | |
| APPLICATIONS | ||
| Tu 11/10 | Lecture 15: Robotics: Perception | 15.1-15.3, 25.3 |
| Th 11/12 | Lecture 16: Natural Language Processing: Text categorization; Information extraction | 23 |
| Th 11/12 | MIDTERM (6 pm) | |
| Tu 11/17 | Lecture 17: Natural Language Processing: Speech. | 15.6 |
| THE AI DREAM | ||
| Th 11/19 | Lecture 18: Logical knowledge representation | 8.2-8.4 |
| Tu 12/1 | Lecture 19: AI and the brain | |
| Th 12/3 | Lecture 20: Conclusion | |
| F 12/11 | PROJECT PRESENTATIONS (12:15pm-3:15pm) |
Readings specified are from the course textbook (Russell & Norvig, Second Edition).
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