About This Course

Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning,[1] together with applications such as data mining and knowledge discovery in databases (KDD), and is often used interchangeably with these terms. However, these are distinguis...

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Course Curriculum

25 Sections | 40 Lecture | 36h 32min total length

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Introduction

| 59min 43sec total length

Introduction
59m

Feature Extraction - I

| 59min 43sec total length

Feature Extraction - I
59m

Feature Extraction - II

| 59min 33sec total length

Feature Extraction - II
59m

Feature Extraction - III

| 56min 29sec total length

Feature Extraction - III
56m

Bayes Decision Theory

| 01h 56min total length

Bayes Decision Theory
57m
Bayes Decision Theory (Contd.)
58m

Normal Density and Discriminant Function

| 01h 51min total length

Normal Density and Discriminant Function
52m
Normal Density and Discriminant Function (Contd.)
58m

Bayes Decision Theory - Binary Features

| 51min 47sec total length

Bayes Decision Theory - Binary Features
51m

Maximum Likelihood Estimation

| 54min 17sec total length

Maximum Likelihood Estimation
54m

Probability Density Estimation

| 04h 41min total length

Probability Density Estimation
56m
Probability Density Estimation (Contd.)
58m
Probability Density Estimation (Contd. )
53m
Probability Density Estimation ( Contd.)
56m
Probability Density Estimation ( Contd. )
57m

Dimensionality Problem

| 57min 38sec total length

Dimensionality Problem
57m

Multiple Discriminant Analysis

| 02h 45min total length

Multiple Discriminant Analysis
57m
Multiple Discriminant Analysis (Tutorial)
54m
Multiple Discriminant Analysis (Tutorial )
53m

Perceptron Criterion

| 01h 48min total length

Perceptron Criterion
54m
Perceptron Criterion (Contd.)
54m

MSE Criterion

| 54min 12sec total length

MSE Criterion
54m

Linear Discriminator (Tutorial)

| 58min 35sec total length

Linear Discriminator (Tutorial)
58m

Neural Networks for Pattern Recognition

| 01h 57min total length

Neural Networks for Pattern Recognition
6m
Neural Networks for Pattern Recognition (Contd.)
58m
Neural Networks for Pattern Recognition (Contd. )
52m

RBF Neural Network

| 01h 50min total length

RBF Neural Network
57m
RBF Neural Network (Contd.)
53m

Support Vector Machine

| 55min 10sec total length

Support Vector Machine
55m

Hyperbox Classifier

| 01h 50min total length

Hyperbox Classifier
53m
Hyperbox Classifier (Contd.)
56m

Fuzzy Min Max Neural Network for Pattern Recognition

| 55min 27sec total length

Fuzzy Min Max Neural Network for Pattern Recognition
55m

Reflex Fuzzy Min Max Neural Network

| 54min 16sec total length

Reflex Fuzzy Min Max Neural Network
54m

Unsupervised Learning -Clustering

| 53min 46sec total length

Unsupervised Learning - Clustering
53m

Clustering (Contd.)

| 52min 42sec total length

Clustering (Contd.)
52m

Clustering using minimal spanning tree

| 56min 52sec total length

Clustering using minimal spanning tree
56m

Temporal Pattern Recognition

| 56min 05sec total length

Temporal Pattern recognition
56m

Hidden Markov Model

| 02h 54min total length

Hidden Markov Model
55m
Hidden Markov Model (Contd.)
59m
Hidden Markov Model (Contd. )
59m

show 22 more sections

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Introduction

Feature Extraction - I

Feature Extraction - II

Feature Extraction - III

Bayes Decision Theory

Normal Density and Discriminant Function

Bayes Decision Theory - Binary Features

Maximum Likelihood Estimation

Probability Density Estimation

Dimensionality Problem

Multiple Discriminant Analysis

Perceptron Criterion

MSE Criterion

Linear Discriminator (Tutorial)

Neural Networks for Pattern Recognition

RBF Neural Network

Support Vector Machine

Hyperbox Classifier

Fuzzy Min Max Neural Network for Pattern Recognition

Reflex Fuzzy Min Max Neural Network

Unsupervised Learning -Clustering

Clustering (Contd.)

Clustering using minimal spanning tree

Temporal Pattern Recognition

Hidden Markov Model

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