Syllabus: CS 580 Introduction to Artificial Intelligence

Syllabus: CS 580 Introduction to Artificial Intelligence

Fall 2010

Monday 4:30-7:10 pm in The Engineering Building 4457

Prof. Zoran Duric
Office: The Engineering Building 4443
Email: zduric_at_cs_dot_gmu_dot_edu
Office Hours: Monday 3:00 - 4:00 pm, Friday 10:30 - 11:30 am or by appointment

Current information about this course will be kept on a CS 580 web page:

This course is delivered to the Internet section online using Moodle
learning management system with MIST/C, which has replaced the Network
EducationWare (NEW) delivery system. Students in all sections will
have accounts and will be able to play recordings of the lectures and
download the slide files from the Moodle course page. Login
information will be sent to all enrolled students by email, before the
first scheduled class.

Course Text: Artificial Intelligence: A Modern Approach, Russell & Norvig,
             Prentice Hall

Supplementary texts:
	     ANSI Common Lisp, Graham, Prentice-Hall
	     Common LISPcraft, R. Wilensky, Norton Publishing
	     Common Lisp - The Language, G. Steele, Digital Press
	     Artifical Intelligence, Luger & Stubblefield, Addison
	     Artificial Intelligence: A New Synthesis, Nilsson, Morgan

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. 

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

			homework:	30%
			project:	10%
			midterm:	25%
			final:		35%