Revision as of 09:13, 13 April 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.
11-13	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.
19,20,21, 22	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

@@ Line 50: / Line 50: @@
 |-
-| 19,20,[[Lecture21ECE662S10|21]],22
+| 19,20,[[Lecture21ECE662S10|21]], [[Lecture22ECE662S10|22]]
 | 8. Linear Discriminants
 |-