Subject Code: EC6L027	Subject Name: Pattern Recognition	L-T-P: 3-0-0	Credits: 3
Pre-requisite(s): Digital signal processing, Probability and stochastic processes
Introduction to pattern recognition; Bayesian decision theory : Classifiers, Discriminant functions, Decision surface, Normal density and discriminant functions, Parameter estimation methods: Maximum-Likelihood estimation, Gaussian mixture models, Expectation-maximization method, Bayesian estimation, Hidden Markov models: Discrete hidden Markov models, Continuous density hidden Markov models; Dimensionality reduction methods: Fisher discriminant analysis, Principal component analysis; Non-parametric techniques for density estimation: Parzen-window method, K-Nearest Neighbour method, Linear discriminant function based classifiers: Perceptron , Support vector machines, Non-metric methods for pattern classification: Non-numeric data or nominal data Decision trees, Unsupervised learning and clustering: Criterion functions for clustering Algorithms for clustering: K-means, Hierarchical and other methods, Cluster validation. Texts/References Books: R.O.Duda, P.E.Hart and D.G.Stork, “Pattern Classification,” John Wiley, 2001. S.Theodoridis and K.Koutroumbas, “Pattern Recognition,” 4th Ed., Academic Press, 2009. C.M.Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006.