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
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