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.
11-13	6. Parametric Density Estimation Maximum likelihood estimation Bayesian parameter estimation
13-19	7. Non-parametric Density Estimation Parzen Windows K-nearest neighbors The nearest neighbor classification rule.
19-22	8. Linear Discriminants
	9. Non-Linear Discriminant functions Support Vector Machines Artificial Neural Networks Decision Trees
	10. Clustering

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OutlineECE662S10 - Rhea