CS 441/541
Artificial Intelligence
Fall Quarter 2008
Time : Mondays and Wednesdays, 12:00-1:50pm
Location: Clay Building (CLY), Room 204.
Instructor:
Melanie Mitchell,
FAB 120-24, (503) 725-2412, mm-AT-cs.pdx.edu.
Office hours: Mondays and Wednesdays, 2:00-3:00pm, or by appointment.
Course Website: :
http://www.cs.pdx.edu/~mm/ArtificialIntelligenceFall2008/index.html
Prerequisites: CS 202, 311, or equivalent.
Textbook: Artificial Intelligence: A Systems Approach, by M. Tim Jones
Major Topics: History of AI, problem-solving and game-playing as search, knowledge repesentation, logical reasoning, learning and reasoning under uncertainty, biologically-inspired AI, natural-language processing, vision, analogy-making, robotics, philosophy of AI.
Exams: There will be no exams.
Grading: Homework: 50%. There will be eight homework assignments and I will use each students' top seven scores on these for grading. In-class presentation: 20%. Final project, presentation and paper: 30%.
Academic integrity: Students will be responsible for following the PSU Student Conduct Code, and in particular, the policy concerning academic honesty.
Students with disabilities: If you are a student with a disability in need of academic accommodations, you should register with Disability Services for Students and notify the instructor immediately to arrange for support services.
Syllabus (subject to change):|
Date |
Topics |
Homework and Reading |
Monday Sept. 29 |
Class overview |
Reading for next class: |
Wednesday Oct. 1 |
Problem-solving as search. |
Reading for next two classes: Textbook, Chapter 2 pp. 21-26; Chapter 3 pp. 49-80; Chapter 4, p. 90-112. |
Monday Oct. 6 |
Heuristic search. |
Homework 2 (Heuristic Search). Due Monday October 13. |
Wednesday Oct. 8 |
|
Reading for next two classes: Textbook, Chapter 6 pp. 171-176;
Chapter 8 pp. 249-268. |
Monday Oct. 13 |
Overview of machine learning. |
Homework 3 (Game playing). Due Wednesday Oct. 22. |
Wednesday Oct. 15 |
Intro to Bayesian Learning |
A good description of the Naive Bayes classifier can be found in the Wikipedia article. |
Monday Oct. 20 |
Neural Networks II.
Reinforcement learning I. |
Readings for next two classes: |
Wednesday Oct. 22 |
Reinforcement Learning II. |
Homework 4 (Decision trees and Bayesian learning). Due Wednesday
Oct. 29. |
Monday Oct. 27 |
Biologically inspired AI I. |
... |
Wednesday Oct. 29 |
Biologically inspired AI II. |
Homework 5 (Neural networks and vision). Due Wednesday
Nov. 5. |
Monday Nov. 3 |
Knowledge representation and natural language processing I. |
Reading: Textbook, Chapter 5, pp. 143-157. Also Chapter 4 ("N-Grams"), pp. 1-19 from D. Jurafsky and J. H. Martin, Speech and Language Processing (on electronic reserve). |
Wednesday Nov. 5 |
Knowledge representation and natural language processing II. |
Optional reading:
An analysis of the AskMSR question-answering system |
Monday Nov. 10 |
Vision: Content-Based Image Retrieval. |
Homework 6 (Genetic algorithms). Due Monday Nov. 24. |
Wednesday Nov. 12 |
Analogy-making. |
... |
Monday Nov. 17 |
Reasoning under uncertainty I. |
Reading: S. Wooldridge, Bayesian Belief Networks |
Wednesday Nov. 19 |
Reasoning under uncertainty II. |
... |
Monday Nov. 24 |
Robotics I. |
Reading: Textbook, chapter 10. |
Wednesday Nov. 26 |
Robotics II. |
Optional reading:
C. Breazeal, (2001). Affective interaction between humans and
robots, in Proceedings of the2001 European Conference on Artificial
Life (ECAL2001). Prague, Czech Rep. |
Monday Dec. 1 |
AI Future and Ethics. |
... |
Wednesday Dec. 3 |
Project presentations: |
... |
Monday Dec. 8 |
No class (finals week). |
... |
Thursday Dec. 11 |
Note: Class time is 12:30pm - 2:20pm |
Final project paper due Friday Dec. 12 |