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Unit-I

Introduction: Basics of pattern recognition, Design principles of pattern recognition system,

Learning and adaptation, Pattern recognition approaches, Mathematical foundations – Linear

algebra, Probability Theory, Expectation, mean and covariance, Normal distribution, multivariate

normal densities, Chi squared test.

Unit-II

Statistical Patten Recognition: Bayesian Decision Theory, Classifiers, Normal density and

discriminant functions,

Unit – III

Parameter estimation methods: Maximum-Likelihood estimation, Bayesian Parameter

estimation, Dimension reduction methods - Principal Component Analysis (PCA), Fisher Linear

discriminant analysis, Expectation-maximization (EM), Hidden Markov Models (HMM),

Gaussian mixture models.

Unit - IV

Nonparametric Techniques: Density Estimation, Parzen Windows, K-Nearest Neighbor

Estimation, Nearest Neighbor Rule, Fuzzy classification.

Unit - V

Unsupervised Learning & Clustering: Criterion functions for clustering, Clustering Techniques:

Iterative square - error partitional clustering – K means, agglomerative hierarchical clustering,

Cluster validation.

References:

1. Richard O. Duda, Peter E. Hart and David G. Stork, "Pattern Classification", 2nd Edition,

John Wiley, 2006.

2. C. M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2009.

3. S. Theodoridis and K. Koutroumbas, "Pattern Recognition", 4th Edition, Academic Press,

2009.