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. ).
Pattern Recognition and Application free videos and free material uploaded by IIT Kharagpur Staff .
Syllabus
Overview
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.
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