Electronics - Pattern Recognition and Application (English)

   Tuesday, April 22, 2025   

Starting a new Lecture Notes Series on Electronics - Pattern Recognition and Application

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Electronics - Pattern Recognition and Application By Lecture Notes together!

Lecture 1: Mod-01 Lec-01 Introduction

Lecture 2: Mod-01 Lec-02 Feature Extraction - I

Lecture 3: Mod-01 Lec-03 Feature Extraction - II

Lecture 4: Mod-01 Lec-04 Feature Extraction - III

Lecture 5: Mod-01 Lec-05 Bayes Decision Theory

Lecture 6: Mod-01 Lec-06 Bayes Decision Theory (Contd.)

Lecture 7: Mod-01 Lec-07 Normal Density and Discriminant Function

Lecture 8: Mod-01 Lec-08 Normal Density and Discriminant Function (Contd.)

Lecture 9: Mod-01 Lec-09 Bayes Decision Theory - Binary Features

Lecture 10: Mod-01 Lec-10 Maximum Likelihood Estimation

Lecture 11: Mod-01 Lec-11 Probability Density Estimation

Lecture 12: Mod-01 Lec-12 Probability Density Estimation (Contd.)

Lecture 13: Mod-01 Lec-13 Probability Density Estimation (Contd. )

Lecture 14: Mod-01 Lec-14 Probability Density Estimation ( Contd.)

Lecture 15: Mod-01 Lec-15 Probability Density Estimation ( Contd. )

Lecture 16: Mod-01 Lec-16 Dimensionality Problem

Lecture 17: Mod-01 Lec-17 Multiple Discriminant Analysis

Lecture 18: Mod-01 Lec-18 Multiple Discriminant Analysis (Tutorial)

Lecture 19: Mod-01 Lec-19 Multiple Discriminant Analysis (Tutorial )

Lecture 20: Mod-01 Lec-20 Perceptron Criterion

Lecture 21: Mod-01 Lec-21 Perceptron Criterion (Contd.)

Lecture 22: Mod-01 Lec-22 MSE Criterion

Lecture 23: Mod-01 Lec-23 Linear Discriminator (Tutorial)

Lecture 24: Mod-01 Lec-24 Neural Networks for Pattern Recognition

Lecture 25: Mod-01 Lec-25 Neural Networks for Pattern Recognition (Contd.)

Lecture 26: Mod-01 Lec-26 Neural Networks for Pattern Recognition (Contd. )

Lecture 27: Mod-01 Lec-27 RBF Neural Network

Lecture 28: Mod-01 Lec-27 RBF Neural Network

Lecture 29: Mod-01 Lec-29 Support Vector Machine

Lecture 30: Mod-01 Lec-30 Hyperbox Classifier

Lecture 31: Mod-01 Lec-31 Hyperbox Classifier (Contd.)

Lecture 32: Mod-01 Lec-32 Fuzzy Min Max Neural Network for Pattern Recognition

Lecture 33: Mod-01 Lec-33 Reflex Fuzzy Min Max Neural Network

Lecture 34: Mod-01 Lec-34 Unsupervised Learning - Clustering

Lecture 35: Mod-01 Lec-35 Clustering (Contd.)

Lecture 36: Mod-01 Lec-36 Clustering using minimal spanning tree

Lecture 37: Mod-01 Lec-37 Temporal Pattern recognition

Lecture 38: Mod-01 Lec-38 Hidden Markov Model

Lecture 39: Mod-01 Lec-39 Hidden Markov Model (Contd.)

Lecture 40: Mod-01 Lec-40 Hidden Markov Model (Contd. )