Pattern Recognition and Application

Week 1 : Introduction
Feature Extraction - I
Feature Extraction - II

Week 2 :Bayes Decision Theory - I
Bayes Decision Theory - II

Week 3 :Normal Density and Discriminant Function - I
Normal Density and Discriminant Function - II
Bayes Decision Theory - Binary Features

Week 4 :Maximum Likelihood Estimation
Probability Density Estimation - I

Week 5 :Probability Density Estimation - II
Probability Density Estimation - III
Probability Density Estimation - IV

Week 6 :Dimensionality Problem
Multiple Discriminant Analysis

Week 7 :Principal Component Analysis - Tutorial
Multiple Discriminant Analysis - Tutorial
Perceptron Criteria - I

Week 8 :Perceptron Criteria - II
MSE Criteria

Week 9 :Linear Discriminator Tutorial
Neural Network - I
Neural Network - II

Week 10 :Neural Network -III/ Hopefield Network
RBF Neural Network - I

Week 11 :RBF Neural Network - II
Support Vector Machine
Clustering -I

Week 12 :Clustering -II
Clustering -III