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Last updated:

September 25, 2023

Duration:

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FREE

This course includes:

Unlimited Duration

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Description

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)

Course Curriculum

  • Lecture 01 – Introduction Unlimited
    • Lecture 02 – Feature Extraction I Unlimited
    • Lecture 03 – Feature Extraction II Unlimited
    • Lecture 04 – Feature Extraction III Unlimited
    • Lecture 05 – Bayes Decision Theory Unlimited
    • Lecture 06 – Bayes Decision Theory (cont.) Unlimited
    • Lecture 07 – Normal Density and Discriminant Function Unlimited
    • Lecture 08 – Normal Density and Discriminant Function (cont.) Unlimited
    • Lecture 09 – Bayes Decision Theory – Binary Features Unlimited
    • Lecture 10 – Maximum Likelihood Estimation Unlimited
    • Lecture 11 – Probability Density Estimation Unlimited
    • Lecture 12 – Probability Density Estimation (cont.) Unlimited
    • Lecture 13 – Probability Density Estimation (cont.) Unlimited
    • Lecture 14 – Probability Density Estimation (cont.) Unlimited
    • Lecture 15 – Probability Density Estimation (cont.) Unlimited
    • Lecture 16 – Dimensionality Problem Unlimited
    • Lecture 17 – Multiple Discriminant Analysis Unlimited
    • Lecture 18 – Multiple Discriminant Analysis (Tutorial) Unlimited
    • Lecture 19 – Multiple Discriminant Analysis (Tutorial) Unlimited
    • Lecture 20 – Perceptron Criterion Unlimited
    • Lecture 21 – Perceptron Criterion (cont.) Unlimited
    • Lecture 22 – Minimum Square Error Criterion Unlimited
    • Lecture 23 – Linear Discriminator (Tutorial) Unlimited
    • Lecture 24 – Neural Networks for Pattern Recognition Unlimited
    • Lecture 25 – Neural Networks for Pattern Recognition (cont.) Unlimited
    • Lecture 26 – Neural Networks for Pattern Recognition (cont.) Unlimited
    • Lecture 27 – RBF (Radial Basis Function) Neural Network Unlimited
    • Lecture 28 – RBF (Radial Basis Function) Neural Network (cont.) Unlimited
    • Lecture 29 – Support Vector Machine Unlimited
    • Lecture 30 – Hyperbox Classifier Unlimited
    • Lecture 31 – Hyperbox Classifier (cont.) Unlimited
    • Lecture 32 – Fuzzy Min-Max Neural Network for Pattern Recognition Unlimited
    • Lecture 33 – Reflex Min-Max Neural Network Unlimited
    • Lecture 34 – Unsupervised Learning – Clustering Unlimited
    • Lecture 35 – Clustering (cont.) Unlimited
    • Lecture 36 – Clustering using Minimum Spanning Tree Unlimited
    • Lecture 37 – Temporal Pattern Recognition Unlimited
    • Lecture 38 – Hidden Markov Model Unlimited
    • Lecture 39 – Hidden Markov Model (cont.) Unlimited
    • Lecture 40 – Hidden Markov Model (cont.) Unlimited

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