Introduction to Artificial Intelligence
Spring 2008

    

This course provides an overview of the theoretical and practical aspects of designing intelligent computer systems. The following topics are covered in the course:

Overview of Artificial Intelligence
            Historical Perspective
            AI in Modern World
State Space Representation
Search Techniques
            Uninformed (Best-first, Depth-first)
            Informed (A*, Best-first)
Search in Games
            Minimax, Alpha-Beta Pruning
Machine Learning
            Classification Trees
            Naïve Bayes
            Neural Networks
Evolutionary Computation/Meta-Heuristics
            Evolutionary/Genetic Algorithms
            Particle Swarm Optimization
            Ant Colony Optimization
Probabilistic Reasoning/Bayesian Networks           
            Knowledge Elicitation
            Inference in BNs

Prerequisites:
CSE205: Data Structures and Abstraction
MTS201: Logic and Discrete Structures

Text Book
Tim Jones, Artificial Intelligence: A Systems Approach, 2007.
Ben Coppin, Artificial Intelligence Illuminated, 2004.

Reference Books
Kevin Korb and Ann Nicholson, Bayesian Artificial Intelligence, 2003

Grading
2 Term Exams                                                 18 marks each
1 Final Exam                                                   40 marks
4 Assignments/Projects (best 3 counts)           4 marks each
4 Quizzes (best 3 counts)                                4 marks each

Software Tools
SWI-Prolog                 (http://www.swi-prolog.org/)
GeNIe                         (http://genie.sis.pitt.edu/)
Weka                         (http://www.cs.waikato.ac.nz/ml/weka/)