Revision as of 09:41, 9 March 2010

Course Outline, ECE662 Spring 2010 Prof. Mimi

Note: This is an approximate outline that is subject to change throughout the semester.

Lecture	Topic
1	1. Introduction
1	2. What is pattern Recognition
2-3	3. Finite vs Infinite feature spaces
4-5	4. Bayes Rule
6-10	5. Discriminant functions Definition; Application to normally distributed features; Error analysis.
11-12	6. Parametric Density Estimation Maximum likelihood estimation Bayesian parameter estimation
	7. Non-parametric Density Estimation Parzen Windows K-nearest neighbors The nearest neighbor classification rule.
	8. Linear Discriminants
	9. Non-Linear Discriminant functions Support Vector Machines Artificial Neural Networks Decision Trees
	10. Clustering

Back to 2010 Spring ECE 662 mboutin

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-. More on Non-Linear Discriminant functions
+. Non-Linear Discriminant functions
 *Support Vector Machines&nbsp;
 *Artificial Neural Networks
 *Decision Trees