| Date | Lecturer | Topic | Reading | Due | Out |
|---|---|---|---|---|---|
| Tu. Aug 28 | Carbonell | Introduction to AI, Basic Search Methods (DFS, BFS, BiBFS, ...) | Ch. 3 | ||
| Th. Aug 30 | Carbonell | Heuristic Functions in Search (HC, Best-first, Beam, B-and-B, A*) | Ch. 4 | Tu. Sep 4 | Carbonell | Beyond Basic Search Methods |
| Th. Sep 6 | Veloso | Bi-polar Search: Minimax, Alpha-beta (lecture on blackboard) |
Ch. 5 | HW1 | |
| Tu. Sep 11 | Veloso | Knowledge Representation - Logic (half of the class cancelled) ( .ps.gz, .pdf) | Ch. 6 | ||
| Th. Sep 13 | Veloso |
Knowledge Representation - Logic II ( .ps.gz, .pdf) |
Ch. 9 | ||
| Tu. Sep 18 | Carbonell | Multi-valued Logics, Non-monotonic Reasoning, Semantic Nets, Frames | HW1 | HW2 | |
| Th. Sep 20 | Carbonell |
Intro to Information Retrieval: Vector Space Methods, Search Engines (hardcopies of the lecture slides also available) |
Ch. 3, Understanding Search Engines (Berry & Browne) | ||
| Tu. Sep 25 | Veloso | Constraint Satisfaction, Iterative Improvement Search | Sec. 4.4 | ||
| Th. Sep 27 | Veloso | Planning I: Situational Calculus | 11.1-11.3 | ||
| Tu. Oct 2 | Veloso |
State-Space Planning ( .ps.gz, .pdf) |
Ch.11-12 | HW2 | HW3 |
| Th. Oct 4 | Veloso |
Plan-Space Planning and More ( .ps.gz, .pdf) |
Ch.11 | ||
| Tu. Oct 9 | Veloso/Carbonell | Midterm Review | HW3 | ||
| Th. Oct 11 | MIDTERM | ||||
| Tu. Oct 16 | Veloso | Conditional Planning | Ch. 13 | ||
| Th. Oct 18 | Carbonell | Rule-based Systems and Knowledge Acquisition | HW4 | ||
| Tu. Oct 23 | Younes (c/Carbonell) | Intro to Machine Learning Methods | |||
| Th. Oct 25 | Carbonell | Intro to Natural Language Processing (Parsing, ATNs, Case Frames) note: only hardcopies of the lecture slides are available; see the course administrator |
Sec. 22.4, 22.6, 22.7 AI encyclopedia article |
||
| Tu. Oct 30 | Carbonell | Basics of Information Theory, Learning Decision Trees | Ch. 18 | HW4 | |
| Th. Nov 1 | Carbonell | Unsupervised Machine Learning: Clustering, Discovery,... | |||
| Tu. Nov 6 | Veloso | Perceptrons and Neural Networks | Ch. 19 | ||
| Th. Nov 8 | Veloso | MDPs and Deterministic Reinforcement Learning | Ch.13 - Mitchell's "Machine Learning" book | HW5 | |
| Tu. Nov 13 | Veloso | Nondeterministic Reinforcement Learning - Policy and Value Iteration | |||
| Th. Nov 15 | Veloso | Exercises in Reinforcement Learning | HW5 | ||
| Tu. Nov 20 | Carbonell | Real-time AI and Applications | |||
| Th. Nov 22 | THANKSGIVING BREAK | ||||
| Tu. Nov 27 | Veloso | Probability Theory and Bayes Networks | Textbook: 14.2,14.3,15.1,15.2 | ||
| Th. Nov 29 | Veloso | Multi-Agent Systems: Layered Learning ( .ps.gz, .pdf) | |||
| Tu. Dec 4 | Veloso | Multi-Robot Systems with Global Perception ( .ps.gz, .pdf) | |||
| Th. Dec 6 | Veloso | Fully Autonomous Multi-Robot Systems ( .ps.gz, .pdf) | |||
| Tu. Dec 11 | All | Review Session ( some problems) | |||
| FINAL EXAM - BH 136A |
Note: Recitations will be scheduled on an as-needed basis (e.g.
a few days before each homework is due, or to review a difficult
topic if there is sufficient demand as judged at the end of class).
Last modified: Monday, November 19, 2001