| Line 1: | Line 1: | ||
=[[ECE662]], Spring 2008= | =[[ECE662]], Spring 2008= | ||
=Lecture 1 Lecture notes= | =Lecture 1 Lecture notes= | ||
| + | Quick link to lecture notes: [[Lecture 1 - Introduction_OldKiwi|1]]| | ||
| + | [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]| | ||
| + | [[Lecture 3 - Bayes classification_OldKiwi|3]]| | ||
| + | [[Lecture 4 - Bayes Classification_OldKiwi|4]]| | ||
| + | [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| | ||
| + | [[Lecture 6 - Discriminant Functions_OldKiwi|6]]| | ||
| + | [[Lecture 7 - MLE and BPE_OldKiwi|7]]| | ||
| + | [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi|8]] | ||
| + | [[Lecture 9 - Linear Discriminant Functions_OldKiwi|9]]| | ||
| + | [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi|10]] | ||
| + | [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|11]]| | ||
| + | [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| | ||
| + | [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi|13]]| | ||
| + | [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| | ||
| + | [[Lecture 15 - Parzen Window Method_OldKiwi|15]]| | ||
| + | [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| | ||
| + | [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| | ||
| + | [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]| | ||
| + | [[Lecture 19 - Nearest Neighbor Error Rates_OldKiwi|19]]| | ||
| + | [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi|20]]| | ||
| + | [[Lecture 21 - Decision Trees(Continued)_OldKiwi|21]]| | ||
| + | [[Lecture 22 - Decision Trees and Clustering_OldKiwi|22]]| | ||
| + | [[Lecture 23 - Spanning Trees_OldKiwi|23]]| | ||
| + | [[Lecture 24 - Clustering and Hierarchical Clustering_OldKiwi|24]]| | ||
| + | [[Lecture 25 - Clustering Algorithms_OldKiwi|25]]| | ||
| + | [[Lecture 26 - Statistical Clustering Methods_OldKiwi|26]]| | ||
| + | [[Lecture 27 - Clustering by finding valleys of densities_OldKiwi|27]]| | ||
| + | [[Lecture 28 - Final lecture_OldKiwi|28]] | ||
---- | ---- | ||
This was the first day of class. These notes are from the class lecture. | This was the first day of class. These notes are from the class lecture. | ||
Revision as of 12:48, 8 March 2012
Contents
ECE662, Spring 2008
Lecture 1 Lecture notes
Quick link to lecture notes: 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
