Pattern Recognition and Application (English)

   Wednesday, October 14, 2020   

Starting a new Lecture Notes Series on Pattern Recognition and Application

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Channel Name: IIT Kharagpur July 2018

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Pattern Recognition and Application By Lecture Notes together!

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Lecture Description of this above Topic:

Lecture 1: NPTEL: Pattern Recognition and Application

Lecture 2: Lecture 01 : Introduction

Lecture 3: Lecture 02 : Feature Extraction - I

Lecture 4: Lecture 03 : Feature Extraction - II

Lecture 5: Lecture 04 : Bayes Decision Theory - I

Lecture 6: Lecture 05 : Bayes Decision Theory - II

Lecture 7: Lecture 06 : Normal Density and Discriminant Function - I

Lecture 8: Lecture 07 : Normal Density and Discriminant Function - II

Lecture 9: Lecture 08 : Bayes Decision Theory - Binary Features

Lecture 10: Lecture 09 : Maximum Likelihood Estimation

Lecture 11: Lecture 10 : Probability Density Estimation - I

Lecture 12: Lecture 11 : Probability Density Estimation - II

Lecture 13: Lecture 12 : Probability Density Estimation - III

Lecture 14: Lecture 13 : Probability Density Estimation - IV

Lecture 15: Lecture 14 : Dimensionality Problem

Lecture 16: Lecture 15 : Multiple Discriminant Analysis

Lecture 17: Lecture 16 : Principal Component Analysis - Tutorial

Lecture 18: Lecture 17 : Multiple Discriminant Analysis - Tutorial

Lecture 19: Lecture 18 : Perceptron Criteria - I

Lecture 20: Lecture 19 : Perceptron Criteria - II

Lecture 21: Lecture 20 : MSE Criteria

Lecture 22: Lecture 21 : Linear Discriminator Tutorial

Lecture 23: Lecture 22 : Neural Network - I

Lecture 24: Lecture 23 : Neural Network - II

Lecture 25: Lecture 24 : Neural Network -III/ Hopfield Network

Lecture 26: Lecture 25 : RBF Neural Network - I

Lecture 27: Lecture 26 : RBF Neural Network - II

Lecture 28: Lecture 27 : Support Vector Machine

Lecture 29: Lecture 28 : Clustering -I

Lecture 30: Lecture 29 : Clustering -II

Lecture 31: Lecture 30 : Clustering -III