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Pattern Recognition and Application

Pattern Recognition and Application. Instructor: Prof. P. K. Biswas, Department of Electronics and Communication Engineering, IIT Kharagpur. This course covers feature extraction techniques and representation of patterns in feature space. Measure of similarity between two patterns. Statistical, nonparametric and neural network techniques for pattern recognition have been discussed in this course. Techniques for recognition of time varying patterns have also been covered. Numerous examples from machine vision, speech recognition and movement recognition have been discussed as applications. Unsupervised classification or clustering techniques have also been addressed in this course. Analytical aspects have been adequately stressed so that on completion of the course the students can apply the concepts learnt in real life problems. (from nptel.ac.in)

Lecture 35 - Clustering (cont.)

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Lecture 01 - Introduction

Feature Extraction

Lecture 02 - Feature Extraction I

Lecture 03 - Feature Extraction II

Lecture 04 - Feature Extraction III

Statistical Pattern Recognition

Lecture 05 - Bayes Decision Theory

Lecture 06 - Bayes Decision Theory (cont.)

Lecture 07 - Normal Density and Discriminant Function

Lecture 08 - Normal Density and Discriminant Function (cont.)

Lecture 09 - Bayes Decision Theory - Binary Features

Maximum Likelihood Estimation

Lecture 10 - Maximum Likelihood Estimation

Probability Density Estimation

Lecture 11 - Probability Density Estimation

Lecture 12 - Probability Density Estimation (cont.)

Lecture 13 - Probability Density Estimation (cont.)

Lecture 14 - Probability Density Estimation (cont.)

Lecture 15 - Probability Density Estimation (cont.)

Dimensionality Problem

Lecture 16 - Dimensionality Problem

Lecture 17 - Multiple Discriminant Analysis

Lecture 18 - Multiple Discriminant Analysis (Tutorial)

Lecture 19 - Multiple Discriminant Analysis (Tutorial)

Linear Discriminant Functions

Lecture 20 - Perceptron Criterion

Lecture 21 - Perceptron Criterion (cont.)

Lecture 22 - Minimum Square Error Criterion

Lecture 23 - Linear Discriminator (Tutorial)

Neural Network Classifier

Lecture 24 - Neural Networks for Pattern Recognition

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

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

Lecture 27 - RBF (Radial Basis Function) Neural Network

Lecture 28 - RBF (Radial Basis Function) Neural Network (cont.)

Lecture 29 - Support Vector Machine

Lecture 30 - Hyperbox Classifier

Lecture 31 - Hyperbox Classifier (cont.)

Lecture 32 - Fuzzy Min-Max Neural Network for Pattern Recognition

Lecture 33 - Reflex Min-Max Neural Network

Unsupervised Classification

Lecture 34 - Unsupervised Learning - Clustering

Lecture 35 - Clustering (cont.)

Lecture 36 - Clustering using Minimum Spanning Tree

Time Varying Pattern Recognition

Lecture 37 - Temporal Pattern Recognition

Lecture 38 - Hidden Markov Model

Lecture 39 - Hidden Markov Model (cont.)

Lecture 40 - Hidden Markov Model (cont.)