1 ECE662, Spring 2008, Prof. Boutin

ECE662, Spring 2008, Prof. Boutin

Introduction

This is the page for the Spring 2008 edition of 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
TA hours: Thursday, 11:45 am-12:45 pm , EE 306

Course Website

Class Lecture Notes

Lecture 1 - Introduction_Old Kiwi
Lecture 2 - Decision Hypersurfaces_Old Kiwi
Lecture 3 - Bayes classification_Old Kiwi
Lecture 4 - Bayes Classification_Old Kiwi
Lecture 5 - Discriminant Functions_Old Kiwi
Lecture 6 - Discriminant Functions_Old Kiwi
Lecture 7 - MLE and BPE_Old Kiwi
Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi
Lecture 9 - Linear Discriminant Functions_Old Kiwi
Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_Old Kiwi
Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi
Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi
Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi
Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi
Lecture 15 - Parzen Window Method_Old Kiwi
Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi
Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi
Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi
Lecture 19 - Nearest Neighbor Error Rates_Old Kiwi
Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi
Lecture 21 - Decision Trees(Continued)_Old Kiwi
Lecture 22 - Decision Trees and Clustering_Old Kiwi
Lecture 23 - Spanning Trees_Old Kiwi
Lecture 24 - Clustering and Hierarchical Clustering_Old Kiwi
Lecture 25 - Clustering Algorithms_Old Kiwi
Lecture 26 - Statistical Clustering Methods_Old Kiwi
Lecture 27 - Clustering by finding valleys of densities_Old Kiwi
Lecture 28 - Final lecture_Old Kiwi

Course Topics

What is Pattern Recognition_Old Kiwi
Bayesian Decision Theory_Old Kiwi
Discriminant Function_Old Kiwi
Parametric Estimators_Old Kiwi
Nonparametric Estimators_Old Kiwi (blank in old QE)
Learning algorithms_Old Kiwi (blank in old QE)
Clustering_Old Kiwi
Clustering Algorithms_Old Kiwi
Feature Extraction_Old Kiwi
Estimation of Classifiability_Old Kiwi
Classifier evaluation_Old Kiwi (blank in old QE)
kNN Algorithm_Old Kiwi
Editing technique_Old Kiwi
Conjugate priors_Old Kiwi
Artificial Neural Networks_Old Kiwi
Probabilistic neural networks_Old Kiwi
Support Vector Machines_Old Kiwi
Mahalanobis Distance_Old Kiwi
ROC curves_Old Kiwi
Decision Tree_Old Kiwi
Metrics and Similarity Measures_Old Kiwi
K continuous derivatives_Old Kiwi
Graph Algorithms_Old Kiwi
Spectral Methods_Old Kiwi

Homework

HW1
HW2
HW3
HW Resources

Applications of Pattern Recognition

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.