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Mod-01 Lec-01 Introduction

Mod-01 Lec-02 Feature Extraction - I

Mod-01 Lec-03 Feature Extraction - II

Mod-01 Lec-04 Feature Extraction - III

Mod-01 Lec-05 Bayes Decision Theory

Mod-01 Lec-06 Bayes Decision Theory (Contd.)

Mod-01 Lec-07 Normal Density and Discriminant Function

Mod-01 Lec-08 Normal Density and Discriminant Function (Contd.)

Mod-01 Lec-09 Bayes Decision Theory - Binary Features

Mod-01 Lec-10 Maximum Likelihood Estimation

Mod-01 Lec-11 Probability Density Estimation

Mod-01 Lec-12 Probability Density Estimation (Contd.)

Mod-01 Lec-13 Probability Density Estimation (Contd. )

Mod-01 Lec-14 Probability Density Estimation ( Contd.)

Mod-01 Lec-15 Probability Density Estimation ( Contd. )

Mod-01 Lec-16 Dimensionality Problem

Mod-01 Lec-17 Multiple Discriminant Analysis

Mod-01 Lec-18 Multiple Discriminant Analysis (Tutorial)

Mod-01 Lec-19 Multiple Discriminant Analysis (Tutorial )

Mod-01 Lec-20 Perceptron Criterion

Mod-01 Lec-21 Perceptron Criterion (Contd.)

Mod-01 Lec-22 MSE Criterion

Mod-01 Lec-23 Linear Discriminator (Tutorial)

Mod-01 Lec-24 Neural Networks for Pattern Recognition

Mod-01 Lec-25 Neural Networks for Pattern Recognition (Contd.)

Mod-01 Lec-26 Neural Networks for Pattern Recognition (Contd. )

Mod-01 Lec-27 RBF Neural Network

Mod-01 Lec-28 RBF Neural Network (Contd.)

Mod-01 Lec-29 Support Vector Machine

Mod-01 Lec-30 Hyperbox Classifier

Mod-01 Lec-31 Hyperbox Classifier (Contd.)

Mod-01 Lec-32 Fuzzy Min Max Neural Network for Pattern Recognition

Mod-01 Lec-33 Reflex Fuzzy Min Max Neural Network

Mod-01 Lec-34 Unsupervised Learning - Clustering

Mod-01 Lec-35 Clustering (Contd.)

Mod-01 Lec-36 Clustering using minimal spanning tree

Mod-01 Lec-37 Temporal Pattern recognition

Mod-01 Lec-38 Hidden Markov Model

Mod-01 Lec-39 Hidden Markov Model (Contd.)

Mod-01 Lec-40 Hidden Markov Model (Contd. )

Description:

Instructor: Prof. P. K. Biswas, Department of Electronics and Communication Engineering, IIT Kharagpur. This course covers feature extraction techniques and the 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.

Pattern Recognition and Application

NPTEL

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#Computer Science #Artificial Intelligence #Neural Networks #Machine Learning #Hidden Markov Models #Feature Extraction #Clustering #Pattern Recognition