**ECE662**: Statistical Pattern Recognition and Decision Making Processes

(cross-listed with computer science as CS662)

This course was previously developed and taught by Professor Keinosuke Fukunaga.

Since 2006, it is taught by Prof. Boutin every Spring of even years.

## Contents

## Textbooks

"Introduction to Statistical Pattern Recognition" by K. Fukunaga

## Peer Legacy

Share advice with future students regarding ECE662 on this page.

## Slectures and Lecture Notes

- Spring 2008, Prof. Boutin, notes collectively written by the students in the class.
- The Boutin Lectures on Statistical Pattern Recognition, Multilingual Slectures by Students in the Spring 2014 Class of ECE662

## Some Course Topics

- Bayes_Decision_Theory
- Fisher Linear Discriminant
- Feature Extraction
- Artificial Neural Networks
- Support Vector Machines
- Clustering
- Decision Trees

## Interesting pages in the ECE662 category

- Decision Theory Glossary
- The effect of adding correlated features
- About Parametric Estimators
- Bayes rule under severe class imbalance
- Fisher linear discriminant can be used for non-linearly separable data too!
- A jump start on using Simulink to develop a ANN-based classifier
- The K Nearest Neighbor Algorithm
- MLE example: binomial and poisson distributions
- MLE example: exponential and geometric distributions
- Video illustrating the decision boundary for normally distributed features

## Semester/Instructor specific pages

- Spring 2016, Prof. Boutin
- Spring 2014, Prof. Boutin
- Spring 2012, Prof. Boutin
- Spring 2010, Prof. Boutin
- Spring 2008, Prof. Boutin

## Other References

- "Pattern Classification" by Duda, Hart, and Stork
- "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler
- "Pattern Recognition and Neural Networks" by Brian Ripley
- "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar