(Course Topics)
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* [[Lecture 19 - Nearest Neighbor Error Rates_OldKiwi]]
 
* [[Lecture 19 - Nearest Neighbor Error Rates_OldKiwi]]
 
* [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi]]
 
* [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi]]
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* [[Lecture 21 - Decision Trees_OldKiwi]]
  
 
==Course Topics==
 
==Course Topics==

Revision as of 11:59, 1 April 2008


Introduction

This is the page for the course ECE662: Pattern Recognition and Decision Making processes.

General Course Information

  • Instructor: Mimi Boutin
  • Office: MSEE342
  • Email: mboutin at purdue dot edu
  • Class meets Tu,Th 9-10:15am in ME118
  • Office hours: Monday, Thursday 4-5pm

Course Website

Course Webpage

Current Kiwi

WebCT

Changelog

Class Lecture Notes

Course Topics

Lots, lots more

Homework

Forum_OldKiwi

Applications of Pattern Recognition_OldKiwi

This page can be used to discuss the applications of pattern recognition in our daily research! This would provide us an intuitive understanding of course topics. Please discuss "applied" pattern recognition here. Instead of just mentioning the field, please explain in detail how a specific tool of pattern recognition can be used in research.

Tools_OldKiwi

Glossary

Reference_OldKiwi

Textbooks

Alumni Liaison

Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett