Revision as of 10:14, 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
22,23,24,25	9. Non-Linear Discriminant functions Support Vector Machines Artificial Neural Networks
26,27,28,29,30	10. Clustering and decision trees

Back to 2010 Spring ECE 662 mboutin

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 | 2. What is pattern Recognition
 |-
 | [[Lecture2ECE662S10|2]],[[Lecture3ECE662S10|3]]
 | 3. Finite vs Infinite feature spaces
 |-
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 | 19,20,[[Lecture21ECE662S10|21]], [[Lecture22ECE662S10|22]]
 | 8. Linear Discriminants
 |-
-|
+| 22,23,24,25
 |
 . Non-Linear Discriminant functions
 *Support Vector Machines&nbsp;
 *Artificial Neural Networks
-*Decision Trees
 |-
-|
+| 26,27,28,29,30
-| 10. Clustering
+| 10. Clustering and decision trees
 |}