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
Overview
Syllabus
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
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
Taught by
Prof. Prabir Kumar Biswas
