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Pattern Recognition and Application

IIT Kharagpur, , Prof. P.K. Biswas

Updated On 02 Feb, 19

Overview

The course has been designed to be offered as an elective to final year under graduate students mainly from Electrical Sciences background. The course syllabus assumes basic knowledge of Signal Processing, Probability Theory and Graph Theory. The course will also be of interest to researchers working in the areas of Machine Vision, Speech Recognition, Speaker Identification, Process Identification etc.The 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.

INTENDED AUDIENCE: Any Interested LearnersPRE-REQUISITES : Nil

Includes

Lecture 22: MSE Criterion

4.1 ( 11 )


Lecture Details

Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit httpnptel.ac.in

Course Details

COURSE LAYOUT Week 1 : Introduction
Feature Extraction - I
Feature Extraction - II Week 2 : Bayes Decision Theory - I Bayes Decision Theory - II Week 3 : Normal Density and Discriminant Function - I Normal Density and Discriminant Function - II
Bayes Decision Theory - Binary Features Week 4 : Maximum Likelihood Estimation Probability Density Estimation - I Week 5 : Probability Density Estimation - II Probability Density Estimation - III
Probability Density Estimation - IV
Week 6 :Dimensionality Problem Multiple Discriminant Analysis
Week 7 :Principal Component Analysis - Tutorial Multiple Discriminant Analysis - Tutorial Perceptron Criteria - I
Week 8 :Perceptron Criteria - II MSE Criteria

Week 9 :Linear Discriminator Tutorial Neural Network - I Neural Network - II
Week 10 :Neural Network -III/ Hopefield Network RBF Neural Network - I Week 11 :RBF Neural Network - II Support Vector Machine Clustering -I
Week 12 :Clustering -II Clustering -III

Ratings

5.0


1 Ratings
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Comments
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Sam

Excellent course helped me understand topic that i couldn't while attendinfg my college.

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Dembe

Great course. Thank you very much.

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