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=Lecture 1 Lecture notes= | =Lecture 1 Lecture notes= | ||
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| + | [[Lecture 1 - Introduction_OldKiwi|1]]| | ||
[[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]| | [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]| | ||
[[Lecture 3 - Bayes classification_OldKiwi|3]]| | [[Lecture 3 - Bayes classification_OldKiwi|3]]| | ||
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[[Lecture 6 - Discriminant Functions_OldKiwi|6]]| | [[Lecture 6 - Discriminant Functions_OldKiwi|6]]| | ||
[[Lecture 7 - MLE and BPE_OldKiwi|7]]| | [[Lecture 7 - MLE and BPE_OldKiwi|7]]| | ||
| − | [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi|8]] | + | [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi|8]]| |
[[Lecture 9 - Linear Discriminant Functions_OldKiwi|9]]| | [[Lecture 9 - Linear Discriminant Functions_OldKiwi|9]]| | ||
| − | [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi|10]] | + | [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi|10]]| |
[[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|11]]| | [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|11]]| | ||
[[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| | [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| | ||
Revision as of 07:01, 11 April 2013
ECE662: Statistical Pattern Recognition and Decision Making Processes
Spring 2008, Prof. Boutin
Collectively created by the students in the class
Contents
Lecture 1 Lecture notes
Jump to: Outline| 1| 2| 3| 4| 5| 6| 7| 8| 9| 10| 11| 12| 13| 14| 15| 16| 17| 18| 19| 20| 21| 22| 23| 24| 25| 26| 27| 28
This was the first day of class. These notes are from the class lecture.
Links to Course Webpages
Login: Use your Purdue Career Account username and password.
Note: You must change your password once a month.
Kiwi Week
Monday at noon until Monday at noon.
Textbook Information
Main article: Textbooks_OldKiwi
There is not a single book that covers all the things that will be discussed in ECE 662. The class will reference four books during the course of the semester as we cover various topics. All four of them are available through the reserves at the engineering library.
Definition and Examples of Pattern Recognition
Main article: What is Pattern Recognition_OldKiwi.
Pattern Recognition is the art of assigning classes or categories to data.
Decision Surfaces
Main Article: Decision Surfaces_OldKiwi
Decision surfaces are the boundaries in the feature space that distinguish classes.
Algebraic Geometry
Main Article: Decision Surfaces_OldKiwi (This is not a typo)
Varieties
Main Article: Varieties_OldKiwi
A Variety is a mathematical construct used to define a decision surface.
Next: Lecture 2
