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Course11710510130 lectures

Pattern Recognition and Application

Prof. Prabir Kumar Biswas · IIT Kharagpur

Start with lecture 1 At NPTEL

About

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 Learners

Lectures

Week 1

  1. Lecture 01 : Introduction YouTube
  2. Lecture 02 : Feature Extraction - I YouTube
  3. Lecture 03 : Feature Extraction - II YouTube

Week 2

  1. Lecture 04 : Bayes Decision Theory - I YouTube
  2. Lecture 05 : Bayes Decision Theory - II YouTube

Week 3

  1. Lecture 06 : Normal Density and Discriminant Function - I YouTube
  2. Lecture 07 : Normal Density and Discriminant Function - II YouTube
  3. Lecture 08 : Bayes Decision Theory - Binary Features YouTube

Week 4

  1. Lecture 09 : Maximum Likelihood Estimation YouTube
  2. Lecture 10 : Probability Density Estimation - I YouTube

Week 5

  1. Lecture 11 : Probability Density Estimation - II YouTube
  2. Lecture 12 : Probability Density Estimation - III YouTube
  3. Lecture 13 : Probability Density Estimation - IV YouTube

Week 6

  1. Lecture 14 : Dimensionality Problem YouTube
  2. Lecture 15 : Multiple Discriminant Analysis YouTube

Week 7

  1. Lecture 16 : Principal Component Analysis - Tutorial YouTube
  2. Lecture 17 : Multiple Discriminant Analysis - Tutorial YouTube
  3. Lecture 18 : Perceptron Criteria - I YouTube

Week 8

  1. Lecture 19 : Perceptron Criteria - II YouTube
  2. Lecture 20 : MSE Criteria YouTube

Week 9

  1. Lecture 21 : Linear Discriminator Tutorial YouTube
  2. Lecture 22 : Neural Network - I YouTube
  3. Lecture 23 : Neural Network - II YouTube

Week 10

  1. Lecture 24 : Neural Network -III/ Hopfield Network YouTube
  2. Lecture 25 : RBF Neural Network - I YouTube

Week 11

  1. Lecture 26 : RBF Neural Network - II YouTube
  2. Lecture 27 : Support Vector Machine YouTube
  3. Lecture 28 : Clustering -I YouTube

Week 12

  1. Lecture 29 : Clustering -II YouTube
  2. Lecture 30 : Clustering -III YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kharagpur
Department
Electrical Engineering
Teacher
Prof. Prabir Kumar Biswas
Level
Undergraduate
Language
English
Subjects
Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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