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| 2. What is pattern Recognition | | 2. What is pattern Recognition | ||
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| − | | [[Lecture2ECE662S10|2]],[[Lecture3ECE662S10|3]] | + | | [[Lecture2ECE662S10|2]],[[Lecture3ECE662S10|3]] |
| 3. Finite vs Infinite feature spaces | | 3. Finite vs Infinite feature spaces | ||
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| Line 33: | Line 33: | ||
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| − | | 11 | + | | [[Lecture11ECE662S10|11]],12,13 |
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6. Parametric Density Estimation | 6. Parametric Density Estimation | ||
| Line 50: | Line 50: | ||
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| − | | 19,20,[[Lecture21ECE662S10|21]], [[Lecture22ECE662S10|22]] | + | | 19,20,[[Lecture21ECE662S10|21]], [[Lecture22ECE662S10|22]] |
| 8. Linear Discriminants | | 8. Linear Discriminants | ||
|- | |- | ||
| − | | | + | | [[Lecture22ECE662S10|22]], [[Lecture23ECE662S10|23]] ,[[Lecture24ECE662S10|24]],[[Lecture25ECE662S10|25]],[[Lecture26ECE662S10|26]] |
| | | | ||
9. Non-Linear Discriminant functions | 9. Non-Linear Discriminant functions | ||
*Support Vector Machines | *Support Vector Machines | ||
| − | *Artificial Neural Networks | + | *Artificial Neural Networks |
| − | + | ||
|- | |- | ||
| − | | | + | | 27,28,29,30 |
| − | | 10. Clustering | + | | 10. Clustering and decision trees |
|} | |} | ||
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
|
| 11,12,13 |
6. Parametric Density Estimation
|
| 13-19 |
7. Non-parametric Density Estimation
|
| 19,20,21, 22 | 8. Linear Discriminants |
| 22, 23 ,24,25,26 |
9. Non-Linear Discriminant functions
|
| 27,28,29,30 | 10. Clustering and decision trees |
Back to 2010 Spring ECE 662 mboutin
