Latest revision as of 07:55, 22 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.
11,12,13	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
22, 23 ,24,25,26	9. Non-Linear Discriminant functions Support Vector Machines Artificial Neural Networks
27,28,29,30	10. Clustering and decision trees

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
@@ Line 33: / Line 33: @@
 |-
-| 11-13
+| [[Lecture11ECE662S10|11]],12,13
 |
 . Parametric Density Estimation
@@ Line 50: / Line 50: @@
 |-
-| 19,20,[[ECE662Sp10_MakeupLectureNotes01|21]],22
+| 19,20,[[Lecture21ECE662S10|21]], [[Lecture22ECE662S10|22]]
 | 8. Linear Discriminants
 |-
-|
+| [[Lecture22ECE662S10|22]], [[Lecture23ECE662S10|23]] ,[[Lecture24ECE662S10|24]],[[Lecture25ECE662S10|25]],[[Lecture26ECE662S10|26]]
 |
 . Non-Linear Discriminant functions
 *Support Vector Machines&nbsp;
 *Artificial Neural Networks
-*Decision Trees
 |-
-|
+| 27,28,29,30
-| 10. Clustering
+| 10. Clustering and decision trees
 |}