Syllabus for CS 480: Artificial Intelligence Spring 2009

Professor: Dr. Alan Garvey

Section 01: MWF 9:30-10:20am

Office Hours

• M-F 10:30am-12:30pm

Contact Information:

• Office: Violette Hall Rm 2166
• Office Phone: x7600
• Email: agarvey@truman.edu

Textbook

• Primary text: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Prentice Hall, 2003, Second Edition (NOT the First Edition, from 1995).
• As we will discuss in class, a Python book might be useful. Note that the links below refer to complete online versions of the books available through Safari from a Truman IP address.
• Two Python books that I like are:
  • Learning Python by Mark Lutz, O'Reilly Media Inc. 2008, Third Edition
  • Python in a Nutshell by Alex Martelli, O'Reilly, 2006, Second Edition (This is a good reference, but it is no longer available in Truman's Safari subscription.)
• Online Python documentation can also be a helpful reference.

Prerequisites

The prerequisite for this class is (CS 170 or CS 180) and junior status. I will assume that you have experience with programming a computer. For this class we will use the Python programming language. Some of you know this language, some of you don't. I will teach the class assuming you don't know the language, but, even for those of you who are familiar with it, the subset of Python that we will use is not the same as that taught in CS 170. This is a 400-level computer science class and I will expect you to be able to learn things on your own outside of class, as well as keep up with material that is presented at a fairly rapid pace in class.

Course Objectives

1. Broadly understand what artificial intelligence is, what it might mean for a machine to be "intelligent" and perhaps gain some insight into human intelligence.
2. Introduce many of the major topics that have been part of artificial intelligence research.
3. Write programs in Python to explore some of the major AI ideas.
4. Explore a particular AI topic in more detail through a significant software project.

Class Attendance

Class attendance is your responsibility. I understand that you will occasionally have to miss class. However, whether you are in class or not you will be responsible for all deadlines and all materials taught or assigned. Graded activities such as tests, quizzes, in-class projects, etc. . . may not be made up. Exceptions to this will be at my discretion and must be arranged with me before the missed class. Be aware that in class I will cover significant amounts of material that is not covered by the textbook.

Grading

• 35% for homework and programming assignments (roughly six to eight during the semester)
• 40% for two in-class exams and the cumulative final
• 25% for your semester-long team project (project will be evaluated at a few points during the semester)

Honesty

Anyone submitting work to be graded which, in my estimation and beyond reasonable doubt, is not his or her work alone will receive an F. No group work is allowed unless I explicitly indicate that you can work in groups. When you do hand in group work, you must always indicate that it is group work and who was involved in it. You are welcome to discuss assignments with anyone, but all work you hand in must be your own. Corrolary: If you provide work you produce to others, you are aiding and abetting their dishonesty and thus being dishonest yourself. Providing your work to others or giving answers to others is not acceptable.