Unit I
Introduction: Applications of pattern recognition, statistical decision theory, image processing and analysis.Probability: Introduction, probability of events, random variables, Joint distributions and densities, moments of random variables, estimation of parameters from samples, minimum risk estimators.

Unit II
Statistical Decision Making: Introduction, Baye’s Theorem, multiple features, conditionally independent features, decision boundaries, unequal costs of error, estimation of error rates, the leaving-one—out technique. Characteristic curves, estimating the composition of populations.

Unit III
Nonparametric Decision Making: Introduction, histograms, Kernel and window estimators, nearest neighbor classification techniques, adaptive decision boundaries, adaptive discriminate Functions, minimum squared error discriminate functions, choosing a decision making technique.

Unit IV
Clustering: Introduction, hierarchical clustering, partitional clustering, Artificial Neural Networks: Introduction, nets without hidden layers. nets with hidden layers, the back Propagation algorithms, Hopfield nets, an application.

Unit V
Processing of Waveforms and Images: Introduction, gray level sealing transformations, equalization, geometric image and interpolation, Smoothing, transformations,edge detection, Laplacian and sharpening operators, line detection and template matching, logarithmic gray level sealing, the statistical significance of image features.

Text Book:
1."Pattern Recognition and Image Analysis"- Eart Gose, Richard Johnsonburg and Steve Joust - Prentice-Hall of India-2003.

References Books:
1."Pattern Classification" Richard O. Duda, Peter E. Hart and David G. Stork - 2nd Edition - John Wiley - 2000 - ISBN: 978-0-471-05669-0.