Introduction:Feature extraction and Pattern Representation – Concept of Supervised and Unsupervised Classification – Introduction to Application Areas;Statistical Pattern Recognition:Bayes Decision Theory – Minimum Error and Minimum Risk Classifiers – Discriminant Function and Decision Boundary – Normal Density – Discriminant Function for Discrete Features – Parameter Estimation;Dimensionality Problem:Dimension and accuracy – Computational Complexity – Dimensionality Reduction – Fisher Linear Discriminant – Multiple Discriminant Analysis;Nonparametric Pattern Classification:Density Estimation – Nearest Neighbour Rule – Fuzzy Classification;Linear Discriminant Functions:Separability – Two Category and Multi Category Classification – Linear Discriminators – Perceptron Criterion – Relaxation Procedure – Minimum Square Error Criterion – Widrow-Hoff Procedure – Ho-Kashyap Procedure – Kesler’s Construction;Neural Network Classifier:Single and Multilayer Perceptron – Back Propagation Learning – Hopfield Network – Fuzzy Neural Network;Time Varying Pattern Recognition:First Order Hidden Markov Model – Evaluation – Decoding – Learning;Unsupervised Classification:Clustering – Hierarchical Clustering – Graph Based Method – Sum of Squared Error Technique – Iterative Optimization

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Course Curriculum

Introduction Details 59:43
Feature Extraction – I Details 54:31
Feature Extraction – II Details 59:33
Feature Extraction – III Details 56:29
Bayes Decision Theory Details 57:13
Bayes Decision Theory (Contd.) Details 58:50
Normal Density and Discriminant Function Details 52:45
Normal Density and Discriminant Function (Contd.) Details 58:32
Bayes Decision Theory – Binary Features Details 51:47
Maximum Likelihood Estimation Details 54:17
Probability Density Estimation Details 56:55
Probability Density Estimation (Contd.) Details 58:23
Probability Density Estimation (Contd. ) Details 53:12
Probability Density Estimation ( Contd.) Details 56:10
Probability Density Estimation ( Contd. ) Details 57:18
Dimensionality Problem Details 57:38
Multiple Discriminant Analysis Details 54:19
Multiple Discriminant Analysis (Tutorial) Details 54:47
Multiple Discriminant Analysis (Tutorial ) Details 0:53
Perceptron Criterion Details 54:14
Perceptron Criterion (Contd.) Details 54:12
MSE Criterion Details 1:57:55
Linear Discriminator (Tutorial) Details 58:35
Neural Networks for Pattern Recognition Details 6:16
Neural Networks for Pattern Recognition (Contd.) Details 58:33
Neural Networks for Pattern Recognition (Contd. ) Details 52:54
RBF Neural Network Details 57:46
RBF Neural Network (Contd.) Details 53:5
Support Vector Machine Details 55:10
Hyperbox Classifier Details 53:59
Hyperbox Classifier (Contd.) Details 56:23
Fuzzy Min Max Neural Network for Pattern Recognition Details 55:27
Reflex Fuzzy Min Max Neural Network Details 54:16
Unsupervised Learning – Clustering Details 53:46
Clustering (Contd.) Details 52:42
Clustering using minimal spanning tree Details 56:52
Temporal Pattern recognition Details 56:5
Hidden Markov Model Details 55:42
Hidden Markov Model (Contd.) Details 59:10
Hidden Markov Model (Contd. ) Details 50:56

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