CIS 471/571, Artificial Intelligence

CIS471/571, Winter 2006

Introduction to Artificial Intelligence

[ Assignments | CIS | News | Grades ]

Course Description: CIS471/571 offers an introduction to ideas, issues, representations and algorithms in the field of artificial intelligence. The course is organized into units covering intelligent agent architectures, problem solving, search, game playing, knowledge representation and reasoning, planing, learning and robotics. Although students are encouraged to use Java to implement programs in assignments, any programming language and existing softwares can be used for course projects.
Time and Place: Tuesdays and Thursdays at 2:00pm - 3:20pm in 301 Gerlinger
Instructor: Dejing Dou, 303 Deschutes, phone 346-4572, email dou@cs.uoregon.edu.
GTF: Jason Galbraith, 205 Deschutes, phone 346-1371, email jgalbrai@cs.uoregon.edu.
Office hours:
- Dejing Fridays 2-3:30 or by appointment (I like to talk with students).
- Jason Wednesdays 3:30-4:30.
Directory: The (unix) directory for the class is /cs/classes/www/06W/cis471.
News: Keep an eye on the course news for miscellaneous announcements.
Evaluation:
- The homework will be a combination of written and programming work.
- The Course Project will be due in parts: Part 1 (proposal) is due February 24 and Part 2 (final report) on March 24. We assume that you will work on it individually. If you wish to work in a group, please check with the instructor.
The approximate weighting of the work done will be

Homework Assignments 40%

Midterm (Feb 16) 20%

Final (Mar 22) 20%

Course Project 20%
Text:
- (Required) Stuart Russel and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd Edition, Prentice Hall, 2003.(text website)

Approximate Syllabus and Lecture Slides

Week of	Chapter	Description and Slides	Events

Jan 9	1,2	intro, intelligent agents;
Jan 16	3,4	problem solving, search
Jan 23	5,6,7	CSP, game playing, logical agents
Jan 30	7,8,9	first order logic and inference
Feb 6	9,10	knowledge representation, other logics, semantic web
Feb 13	10.3,11.1,11.2	more KR and planning	Midterm Thursday, Feb 16
Feb 20	11.3,13,14	more planning, uncertainty and bayesian network	Course Project Part 1: Fri, Feb 24
Feb 27	18,19	machine learning
Mar 6	20.5, 25	learning by neural network, robotics
Mar 13		final review
Mar 20		finals week;	Final exam Wed 1pm-3pm, Mar 22; Course Project Part 2: Fri, Mar 24

Late policy: All assignments are due by 5:00pm of the stated due date, though it is much safer to turn them in at class time. Late papers are subject to a 10% per day penalty. Late assignments will not be accepted once the graded papers are returned or once sample solutions are handed out. It is your responsibility to turn in the assignments on time. Make sure that electronically submitted assignments are actually delivered.
Course resources:
- Text website.
- AI Information Site

Homework Assignments	40%
Midterm (Feb 16)	20%
Final (Mar 22)	20%
Course Project	20%