Date | Lecturer | Topic | Reading | Due | Out |
---|---|---|---|---|---|
Tu. Aug 29 | Carbonell |
Introduction to AI, Basic Search Methods (DFS, BFS, BiBFS, ...) note: only a subset of the lecture slides are available online; see the course administrator for a complete copy additional handout: syllabus |
Ch. 3 | ||
Th. Aug 31 | Carbonell | Heuristic Functions in Search (HC, Best-first, Beam, B-and-B, A*) | Ch. 4 | HW1 | |
Tu. Sep 5 | Carbonell | Beyond Basic Search Methods | |||
Th. Sep 7 | Veloso | Bi-polar Search (Minimax, Alpha-beta), Game playing |
Ch. 5 | ||
Tu. Sep 12 | Veloso | Knowledge Representation, First-order Logic | Ch. 6 | ||
Th. Sep 14 | Veloso | Resolution, Unification, Model-based Reasoning | Ch. 9 - resolution for propositional logic only, normal form |
HW1 | HW2 |
Tu. Sep 19 | Carbonell | Multi-valued Logics, Non-monotonic Reasoning, Semantic Nets, Frames | |||
Th. Sep 21 | 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. Sep 26 | Veloso | Constraint Satisfaction, Constraint-based Scheduling | Sec. 4.4 | ||
Th. Sep 28 | Veloso | Planning I: State-Space Planning | HW2 | HW3 | |
Tu. Oct 3 | Veloso | Planning II: Plan-Space Planning, Comparison | Ch. 11 | ||
Th. Oct 5 | Veloso | Planning III: Graphplan, Satplan, Comparison (Lecture slides included in Planning II above) | |||
Tu. Oct 10 | Veloso/Carbonell | Midterm Review | HW3 | ||
Th. Oct 12 | MIDTERM Midterm exam solutions | ||||
Tu. Oct 17 | Veloso | Conditional Planning | Ch. 13 | HW4 | |
Th. Oct 19 | Veloso | Earthware Symposium, McConomy Auditorium | |||
Tu. Oct 24 | Prof. Yiming Yang (guest lecturer) |
Decision Trees and Text Categorization | Ch. 18 | ||
Th. Oct 26 | Carbonell | Intro to Information Retrieval: Vector Space Methods, Search Engines | |||
Tu. Oct 31 | Carbonell | Rule-based Systems and Knowledge Acquisition | |||
Th. Nov 2 | Carbonell | Supervised and Unsupervised Machine Learning Methods | |||
Tu. Nov 7 | Veloso | Neural Networks | Ch. 19 | HW4 | HW5 |
Th. Nov 9 | Veloso | MDPs and Deterministic Reinforcement Learning | Ch.13 - Mitchell's "Machine Learning" book | ||
Tu. Nov 14 | Veloso | Nondeterministic Reinforcement Learning (Lecture slides included in previous class) | |||
Th. Nov 16 | Veloso | Exercises in Reinforcement Learning | |||
Tu. Nov 21 | Veloso | Probability Theory and Bayes Networks | Textbook: 14.2,14.3,15.1,15.2 | HW5 | HW6 |
Th. Nov 23 | THANKSGIVING BREAK | ||||
Tu. Nov 28 | Carbonell | Real-time AI and Applications | |||
Th. Nov 30 | Veloso | Multi-Agent Systems: Layered Learning | |||
Tu. Dec 5 | Veloso | Multi-Robot Systems with Global Perception | |||
Th. Dec 7 | Veloso | Fully Autonomous Multi-Robot Systems | HW6 | ||
Tu. Dec 12 | Jensen/Hiyakumoto | Review Session - sample problem solutions part1, part2 | |||
Th. Dec 14 | FINAL EXAM: 8:30am to 11:30am, rooms DH1211 & DH1212 |
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).