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. ).