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

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 . Parametric Density Estimation
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-| [[Lecture22ECE662S10|22]], [[Lecture23ECE662S10|23]] ,[[Lecture24ECE662S10|24]],25
+| [[Lecture22ECE662S10|22]], [[Lecture23ECE662S10|23]] ,[[Lecture24ECE662S10|24]],[[Lecture25ECE662S10|25]],[[Lecture26ECE662S10|26]]
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 . Non-Linear Discriminant functions
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-| 26,27,28,29,30
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 | 10. Clustering and decision trees
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