Saint Mary’s University
Department of Mathematics and Computing Science
CSC 482.1: Introduction to Artificial Intelligence
FALL 1999-2000
Instructor: Dr. Pawan Lingras
Class time: Monday, Wednesday 2:30-3:45 p.m.
Brief Description
This course provides a general introduction to Artificial Intelligence (AI). Artificial intelligence covers a wide range of topics. This course will consider philosophical, mathematical, and experimental/implementation aspects of such topics as problem solving, searching, game playing, genetic algorithms, logic, expert systems, reasoning under uncertainty, fuzzy sets, learning, and neural networks. In addition to theoretical introduction, students will get exposure to some of the popular AI tools. Due to time constraints, it will not be possible to study each of the topics in great detail. Students are expected to conduct an indepth study of any one of the AI topics.
Textbook
Noyes, J. L. (1992) Artificial Intelligence with Common Lisp, D.C. Heath and Company, Toronto. First Edition.
Brief Class Outline:
Introduction to Aritificial Intelligence.
Problem solving, seraching, genetic algorithms.
Learning from observations, neural networks.
Logic and inference.
Expert systems.
Probabilistic and fuzzy systems.
Intelligent communication, natural language processing, speech recognition.
Vision.
Robotics.
Evaluation scheme
Method of Evaluation |
Marks |
| Class Assignments | 20 |
| In-class quizzes (approx. 5) | 5 |
| Project | 15 |
| Midterm | 25 |
| Final | 35 |
Total |
100 |
Assignments and Project
Assignments involve working with one of the well established AI tools. For each assignment, there will be one or two leaders. The leaders (with the help of the instructor) are expected to acquire expertise in the software package and help other students run small test applications. The leader will receive a maximum of 20 marks and others maximum of 10 marks for the assignment. Students who cannot be a leader will have the option of attaching the leadership marks to their projects.
Assignment 1 - Genetic Algorithms (Due Date: September 27,1999)
Assignment 2 - Neural Networks (Due Date: October 13,1999)
Assignment 3 - Expert Systems (Due Date: November 1,1999)
Assignment 4 - Data Mining (Due Date: November 22,1999)
Project (Due Date: Dec. 2, 1999)
Midterm
The midterm examination will be held on Monday, October 18, 1999. A link to the guide for the midterm will be available here.
Final Examination
The final examination will be scheduled by the Registrar’s office during the examination period from December 4-18, 1999. A link to the guide for the final examination will be available here.