Current information about this course will be kept on a CS 480 web page:
http://cs.gmu.edu/~zduric/cs480.html
Course Text: Artificial Intelligence: A Modern Approach, 2nd ed.,
Russell & Norvig, Prentice Hall
Supplementary texts:
ANSI Common Lisp, Graham, Prentice-Hall (recommended)
Common LISPcraft, R. Wilensky, Norton Publishing
Common Lisp - The Language, G. Steele, Digital Press
Prerequisites: A working knowledge of computer systems and several
programming languages is required. The material covered
in CS 310 and CS 330 as well as general computer science
maturity is assumed and used throughout the course.
Content: The basic principles of representation, heuristic search,
learning, and control will be presented in the context
of specific AI areas such as problem solving, vision,
natural language, and expert systems. The Lisp
programming language will be used as the primary
language for homework assignments.
Course Outcomes:
1. A knowledge of basic uninformed and heuristic search techniques.
2. A knowledge of basic logic or probabilistic reasoning techniques.
3. A knowledge of basic machine learning techniques.
4. An ability to implement basic AI methods in Lisp or in Prolog.
5. An ability to identify and apply an appropriate AI method to a given problem.
Exams: There will be a midterm and final exam.
Homework: There will be several programming assignments which will
include written summaries. Other assignments could
include practice problems from the textbook and/or old
exams. A class project will be required.
Grading: The course grade will be determined approximately as
follows:
homework: 30%
project: 20%
midterm: 20%
final: 30%
Zoran Duric (zduric@cs.gmu.edu)