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| − | ! scope="col" | Lecture | + | ! scope="col" | Lecture |
! scope="col" | Topic | ! scope="col" | Topic | ||
|- | |- | ||
| − | | 1 | + | | [[Lecture1ECE662S10|1]] |
| 1. Introduction | | 1. Introduction | ||
|- | |- | ||
| − | | 1 | + | | [[Lecture1ECE662S10|1]] |
| 2. What is pattern Recognition | | 2. What is pattern Recognition | ||
|- | |- | ||
| − | | 2 | + | | [[Lecture2ECE662S10|2]],[[Lecture3ECE662S10|3]] |
| 3. Finite vs Infinite feature spaces | | 3. Finite vs Infinite feature spaces | ||
|- | |- | ||
| − | | 4 | + | | [[Lecture4ECE662S10|4]],[[Lecture5ECE662S10|5]] |
| 4. Bayes Rule | | 4. Bayes Rule | ||
|- | |- | ||
| − | | 6-10 | + | | [[Lecture6ECE662S10|6]]-10 |
| | | | ||
| − | 5. | + | 5. Discriminant functions |
| − | + | *Definition; | |
| − | + | *Application to normally distributed features; | |
| − | + | *Error analysis. | |
| − | + | ||
| − | + | ||
|- | |- | ||
| − | | 11 | + | | [[Lecture11ECE662S10|11]],12,13 |
| | | | ||
| − | 6. Parametric Density Estimation | + | 6. Parametric Density Estimation |
| − | + | *Maximum likelihood estimation | |
| − | + | *Bayesian parameter estimation | |
| − | + | ||
|- | |- | ||
| + | | 13-19 | ||
| | | | ||
| − | + | 7. Non-parametric Density Estimation | |
| − | 7. Non-parametric Density Estimation | + | |
| − | + | *Parzen Windows | |
| − | + | *K-nearest neighbors | |
| − | + | *The nearest neighbor classification rule. | |
| − | + | ||
| − | + | ||
|- | |- | ||
| − | | | + | | 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 | |
| + | |||
| + | *Support Vector Machines | ||
| + | *Artificial Neural Networks | ||
| + | |||
|- | |- | ||
| − | | | + | | 27,28,29,30 |
| − | | 10 | + | | 10. Clustering and decision trees |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
|} | |} | ||
Latest revision as of 08:55, 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
