CSC 4810/6810 (Computer Numbers 1876/1893)

Artificial Intelligence Fall 2001

Classroom: 203-G, 7:15-8:55 p.m., MW


Instructor: Dr. Yanqing Zhang

Office: 776 College of Education Building

Phone: 404-651-0682 (o)

Fax: 404-651-2246



Office Hours: 2:30 4:00 p.m. MW or by appointment


Text: Artificial Intelligence - A Modern Approach , by S. J. Russell and P. Norvig, Prentice Hall, 1995.

Course Content: Introduction to basic AI techniques and methodologies. Topics include search strategies, problem solving, knowledge and reasoning, logic and deduction, expert systems, neural networks, fuzzy logic, genetic algorithms, learning, data mining, intelligent agents, etc. Hands-on programming projects.

Prerequisite: CSc 3410 Data Structures and CSc 4330 Programming Language Concepts.

Course Requirements: All students should not only learn basic theoretical principles but also accumulate practical hands-on experience. Undergraduate students and graduate students will do assignments, take tests and finish programming projects. Team work is encouraged. At the end of this semester, all graduate students and undergraduate students will give presentations to share knowledge and skills. An undergraduate student needs to write a technical report. A graduate student needs to write a conference paper.


Class Policy:

      Attendance: Students are required to attend all classes.

      Academic honesty: Plagiarism will result in a score of zero on the test or paper. The instructor has the right to make a decision of if two or more papers are cheating.

      Assignments and Projects: They must be handed in on time and will not be accepted when past due.

      Withdrawals: Oct. 12 Friday is the last day to withdraw and possibly receive a W.

      Make-ups: Must need the instructor's special permission.

Grading Policy:

Mid-term Exam 25%


A [90, 100]

Final Exam 25%


B [80, 90)

Assignments 15%


C [70, 80)

Projects 30%


D [60, 70)

Attendance 5%


F [0, 60)


Tentative Course Outline and Schedule:

Chapter 1 Introduction

Aug. 20, 22

Chapter 2 Intelligent Agents

Aug. 27, 29

Chapter 3 Solving Problems by Searching

Sept. 5, 10, 12

Chapter 4 Informed Search Methods

Sept. 17, 19

Chapter 5 Game Playing

Sept. 24, 26

Chapter 6 Agents that Reason Logically

Oct. 1, 3

* Mid-term Exam

Oct. 9

Chapter 14 Uncertainty

Oct. 10, 15, 17

Chapter 15 Probabilistic Reasoning Systems

Oct. 22, 24

Chapter 18 Learning from Observations

Oct. 29, 31

Chapter 19 Learning in Neural and Belief Networks

Nov. 5, 7

Chapter 20 Reinforcement Learning

Nov. 12, 14

Chapter 27 AI: Present and Future

Nov. 19

# Project Presentations

Nov. 26, 28, Dec. 3

* Final Exam (Project Due)

Dec. 5 7:15-8:55 p.m. W


Statement: This course syllabus provides a general plan for the course; deviations may be necessary.