Syllabus: CS 580 Introduction to Artificial Intelligence
Syllabus: CS 580 Introduction to Artificial Intelligence
Fall 2012
Tuesday 4:30-7:10 pm in Nguyen Engineering Building 4705
Prof. Zoran Duric
Office: Nguyen Engineering Building 4443
Email: zduric_at_cs_dot_gmu_dot_edu
Office Hours: Monday 3:00 - 4:00 pm, Tuesday 3:00 - 4:00 pm or by appointment
Current information about this course will be kept on a CS 580 web page:
http://cs.gmu.edu/~zduric/cs580.html
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 the online section 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 students enrolled in the online
section 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
Wesley
Artificial Intelligence: A New Synthesis, Nilsson, Morgan
Kaufmann
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
follows:
homework: 30%
project: 10%
midterm: 25%
final: 35%