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| 2. What is pattern Recognition
 
| 2. What is pattern Recognition
 
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| 2-3  
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| [[Lecture2ECE662S10|2]]-3  
 
| 3. Finite vs Infinite feature spaces
 
| 3. Finite vs Infinite feature spaces
 
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Revision as of 07:04, 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

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Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

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