Syllabus: CS 480 Introduction to Artificial Intelligence

Syllabus: CS 480 Introduction to Artificial Intelligence

Spring 2009

TR 12:00 - 1:15pm in IN 134

Prof. Zoran Duric
Office: S&T II, Rm. 427
email: zduric@cs.gmu.edu
Office Hours: Tuesday 2:00-4:00pm, Thursday 1:30-2:30pm or by appointment



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)