Download Pattern Recognition

EC630
Pattern Recognition
3-0-0; Credit: 3;
Lectures- 42
PREREQUISITE

Probability, Random Variable and Stochastic Process, Linear Algebra
COURSE OBJECTIVE
 This course concerns with fundamentals of pattern classification and regression.
COURSE CONTENT
Unit-I: Introduction- Overview of pattern recognition system, Bayesian decision theory, Bayes classifier,
Estimation of Error, Bayes classifier for minimizing risk, Minmax classifier, Neyman-Pearson
classifier, Discriminant function.
Unit-II: Density estimation (Parametric methods)-Overview, Properties of a good estimator, Sufficient
statistics, Maximum-Likelihood (ML) estimation and Bayesian estimation for estimating
different densities, Gaussian mixture model (GMM), Maximum a posteriori (MAP) adaptation,
Hidden Markov model (HMM); Nonparametric methods: Overview, Parzen windows method,
Nearest neighbour method.
Unit-III: Linear Discriminant function, Perceptron learning algorithm, convergence, Fishers linear
discriminant, linear discriminant function for multi-class case, Multi-class logistic regression.
Unit-IV: Multilayer Neural network-Overview, Feed forward Neural networks, Back propagation
Algorithm; Representational abilities of feed forward networks, Feed forward networks for
Classification and Regression, Radial Basis Function Networks; Gaussian RBF networks,
Learning Weights in RBF networks; K-means clustering algorithm.
Unit-V: Support Vector Machines (SVM)-Overview, SVM formulation with slack variables, nonlinear
SVM classifiers, Kernel Functions for nonlinear SVMs, Support Vector Regression and εinsensitive Loss function, Positive Definite Kernels, Representer Theorem, Dimensionality
reduction using PCA and LDA
TEXT BOOKS
1. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.
2. S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall/CRC, 2009.
REFERENCE BOOKS
1. C. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics),
Springer, 2006.
2. I. H. Witten, Data Mining: Practical Machine Learning Tools And Techniques, 2nd Edn.,
Elsevier India, 2008.
COURSE OUTCOMES
Upon successful completion of this course, students should be able to understand the following:
CO1: Pattern recognition techniques for different real world application
CO2: Parametric and non-parametric methods for density estimation
CO3: Development of different classifiers using Bayesian decision theory
CO4: Concepts on Artificial neural network and its applications for different real world tasks
CO5: Concepts on support vector machine and its applications for different real world