(→Homework) |
|||
| Line 55: | Line 55: | ||
* [[Homework 1_Old Kiwi]] | * [[Homework 1_Old Kiwi]] | ||
| + | * [[Homework 2_Old Kiwi]] | ||
| + | |||
| + | [[Category:Homework]] | ||
==[[Forum_Old Kiwi]]== | ==[[Forum_Old Kiwi]]== | ||
Revision as of 10:21, 4 March 2008
Contents
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
Class Lecture Notes
- Lecture 1 - Introduction_Old Kiwi
- Lecture 2 - Decision Hypersurfaces_Old Kiwi
- Lecture 3 - Bayes classification_Old Kiwi
- Lecture 4 - Bayes Classfication_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
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
- Learning algorithms_Old Kiwi
- Clustering_Old Kiwi
- Feature Extraction_Old Kiwi
- Estimation of Classifiability_Old Kiwi
- Classifier evaluation_Old Kiwi
- Artificial Neural Networks_Old Kiwi
- Support Vector Machines_Old Kiwi
- Mahalanobis Distance_Old Kiwi
- ROC curves_Old Kiwi
