NPTEL : Pattern Recognition and Application (Electronics and Communication Engineering)

Co-ordinators : Prof. P.K. Biswas


Lecture 1 - Introduction

Lecture 2 - Feature Extraction - I

Lecture 3 - Feature Extraction - II

Lecture 4 - Feature Extraction - III

Lecture 5 - Bayes Decision Theory

Lecture 6 - Bayes Decision Theory (Continued.)

Lecture 7 - Normal Density and Discriminant Function

Lecture 8 - Normal Density and Discriminant Function (Continued.)

Lecture 9 - Bayes Decision Theory - Binary Features

Lecture 10 - Maximum Likelihood Estimation

Lecture 11 - Probability Density Estimation

Lecture 12 - Probability Density Estimation (Continued.)

Lecture 13 - Probability Density Estimation (Continued.)

Lecture 14 - Probability Density Estimation (Continued.)

Lecture 15 - Probability Density Estimation (Continued.)

Lecture 16 - Dimensionality Problem

Lecture 17 - Multiple Discriminant Analysis

Lecture 18 - Multiple Discriminant Analysis (Tutorial)

Lecture 19 - Multiple Discriminant Analysis (Tutorial)

Lecture 20 - Perceptron Criterion

Lecture 21 - Perceptron Criterion (Continued.)

Lecture 22 - MSE Criterion

Lecture 23 - Linear Discriminator (Tutorial)

Lecture 24 - Neural Networks for Pattern Recognition

Lecture 25 - Neural Networks for Pattern Recognition (Continued.)

Lecture 26 - Neural Networks for Pattern Recognition (Continued.)

Lecture 27 - RBF Neural Network

Lecture 28 - RBF Neural Network (Continued.)

Lecture 29 - Support Vector Machine

Lecture 30 - Hyperbox Classifier

Lecture 31 - Hyperbox Classifier (Continued.)

Lecture 32 - Fuzzy Min Max Neural Network for Pattern Recognition

Lecture 33 - Reflex Fuzzy Min Max Neural Network

Lecture 34 - Unsupervised Learning - Clustering

Lecture 35 - Clustering (Continued.)

Lecture 36 - Clustering using minimal spanning tree

Lecture 37 - Temporal Pattern recognition

Lecture 38 - Hidden Markov Model

Lecture 39 - Hidden Markov Model (Continued.)

Lecture 40 - Hidden Markov Model (Continued.)