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Related Courses


COEN 6321: Applied Genetic and Evolutionary Systems (4 credits)

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 natural selection.
You may require the instructor's permission in addition. Open for fall registration only. Prerequisites: COEN 5301 or any Object-Oriented Programming course.


COEN 691M: Machine Learning, Theory and Engineering Applications (4 credits)

Introduction to Learning and Adaptation in Natural and Artificial Systems. Essential Terms and Fundamental Concepts in Machine Learning: Search Space, Training and Exploitation, Concept Formation, etc. Also, General-to-Specific and Specific-to-General Learning. Rule-based Learning, Decision Tree Learning, Instance-Based (Inductive) Learning, Deductive Learning, Reinforcement Learning, Bayesian Learning, Evolutionary Computation based Learning, Hybrid Systems. The Design of a real-world Learning System. Two (2-3 student) group projects will be chosen by the students, based on problems in: Pattern Recognition, Signal and Image Processing, Digital Communications, Strategy Games, Natural Language Processing, and Artificial Life. Formal Term Paper required. Prerequisites: COEN 5301.


COMP 473/6731: Pattern Recognition (3 credits)

This 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, etc.


COMP 472/ 6721: Introduction to Artificial Intelligence (4 credits)

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