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Course Detail

Course Name Pattern Recognition
Course Code 15CSE361
Program B. Tech. in Computer Science and Engineering
Year Taught 2019


Unit 1

Introduction: Machine perception – Pattern recognition systems – Design cycle – Learning and adaptation – Bayesian decision theory – minimum error rate classification – discriminant functions – decision surfaces – normal density based discriminant functions – Maximum likelihood estimation – Bayesian estimation.

Unit 2

Bayesian parameter estimation – Gaussian case – problems of dimensionality – Components analysis and discriminants – hidden Markov models, Non-parametric Techniques: density estimation – parzen windows – nearest neighbourhood estimation – linear discriminant functions and decision surfaces – two category linearly separable case – perception criterion function.

Unit 3

Non-Metric Methods: decision trees – CART methods – algorithm independent machine learning- bias and variance – regression and classification – classifiers – Unsupervised learning and clustering – mixture densities and identifiably – hierarchical clustering – low dimensional representation – multidimensional scaling.

Text Books

  • Duda R. O., Hart P. E. and Stork D. G., “Pattern Classification”, Second Edition, John Wiley & Sons, 2003


  • Gose E., Johnsonbaugh R. and Jost S., “Pattern Recognition and Image Analysis”, Prentice Hall of India, 2002.
  • Bishop C. M., “Pattern Recognition and Machine Learning (Information Science and Statistics)”, First Edition, Springer, 2006.
  • Bishop C. M., “Neural networks for Pattern Recognition”, Oxford University Press, 1995

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