Applied Genetic and Evolutionary Systems
This course explores
theoretical and practical aspects of genetic and evolutionary algorithms (GA's).
These GA's are search and optimization programs inspired by Darwin's theory of
may require the instructor's permission in addition.
Open for fall registration
Prerequisites: COEN 5301 or any Object-Oriented Programming course.
course is an introduction to the subject of pattern recognition. We will cover
theoretical foundations of classification and pattern recognition and discuss
applications in character, speech and face recognition, and some applications in
automation and robotics. A tentative list of topics includes: Bayesian decision
theory, discriminant functions for normal class distributions, parameter
estimation and supervised learning, nonparametric techniques ( nearest neighbor
rules, Parzen kernel rules, tree classifiers), linear discriminant functions and
learning (perceptron, LMS algorithms, support vector machines), unsupervised
learning and clustering, neural networks including multilayer perceptrons and
radial basis networks, and machine learning.Prerequisites:
COMP 352 or Basic knowledge of statistics and probability theory, elements of
calculus and linear algebra. Standard programming languages such as C++, Java,
COMP 472/ 6721:
Introduction to Artificial Intelligence
Scope of AI. Heuristics. Problem-solving methodologies. Game-playing. Reasoning
by deduction and induction. Natural language processing. Prerequisites: COMP 352
or COMP 5511.
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Last Updated April 6, 2004