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Course Detail

Course Name Pattern Recognition
Course Code 23DLS632
Credits 3


Course outcomes
CO 1 To understand Bayesian decision theory and its use
CO 2 To gain knowledge about Bayesian estimation methods
CO3 To apply nonparametric techniques and linear discriminant functions
CO4 To gain knowledge about nonmetric methods and algorithm independent machine learning
CO5 To apply unsupervised learning and clustering

Pattern recognition systems – the design cycle – learning and adaptation – Bayesian decision theory – continuous features – Minimum error rate classification – discriminant functions and decision surfaces – the normal density based discriminant functions. Bayesian parameter estimation – Gaussian case and general theory – problems of dimensionality – components analysis and discriminants- Nonparametric techniques – density estimation – Parzen windows – nearest neighborhood estimation – rules and metrics – decision trees – CART methods – algorithm-independent machine learning – bias and variance for regression and classification – resampling or estimating statistics- Unsupervised learning and clustering – mixture densities and identifiability – maximum likelihood estimates – application to normal mixtures – unsupervised Bayesian learning – data description and clustering – criterion functions for clustering – hierarchical clustering – k-means clustering.

Text Reference Book

  1. Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classification”, Second Edition, 2003, John wily & sons.
  2. Earl Gose, Richard Johnsonbaugh and Steve Jost, “Pattern Recognition and Image Analysis, 2002, Prentice Hall of India.

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