Revision as of 07:19, 12 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 18: / Line 18: @@
 | 2. What is pattern Recognition
 |-
-| [[Lecture2ECE662S10|2]]-3
+| [[Lecture2ECE662S10|2]],[[Lecture3ECE662S10|3]]
 | 3. Finite vs Infinite feature spaces
 |-
-| 4-5
+| [[Lecture4ECE662S10|4]],[[Lecture5ECE662S10|5]]
 | 4. Bayes Rule
 |-
-| 6-10
+| [[Lecture6ECE662S10|6]]-10
 |
 . Discriminant functions