Revision as of 08:39, 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. More on Non-Linear Discriminant functions Support Vector Machines Artificial Neural Networks Decision Trees
	10. Clustering

Back to 2010 Spring ECE 662 mboutin

@@ Line 7: / Line 7: @@
 <br>
-{| width="55%" border="1" cellpadding="1" cellspacing="1"
+{| width="55%" cellspacing="1" cellpadding="1" border="1"
 |-
 ! scope="col" | Lecture
 ! scope="col" | 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
 |
-. Discriminate functions
+. Discriminant functions
-- Definition;
+*Definition;
+*Application to normally distributed features;
-- Application to normally distributed features;
+*Error analysis.
-- Error analysis.
 |-
 | 11-12
 |
 . Parametric Density Estimation
--Maximum likelihood estimation
+*Maximum likelihood estimation
+*Bayesian parameter estimation
--Bayesian parameter estimation
 |-
 |
 |
 . Non-parametric Density Estimation
--Parzen Windows
+*Parzen Windows
+*K-nearest neighbors
--K-nearest neighbors
+*The nearest neighbor classification rule.
--The nearest neighbor classification rule.
 |-
@@ Line 59: / Line 54: @@
 |-
 |
-| 9. SVM
-|-
 |
-| 10. ANN
+. More on Non-Linear Discriminant functions
-|-
-|
+*Support Vector Machines&nbsp;
-| 11. Decision Trees
+*Artificial Neural Networks
+*Decision Trees
 |-
 |
-| 12. Clustering
+| 10. Clustering
 |}