| Course Name | Statistical Pattern Classifications |
Introduction and Bayesian Decision Theory
Introduction – 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.
Maximum-likelihood and Bayesian Parameter Estimation
Maximum likelihood estimation – Bayesian estimation – Bayesian parameter estimation – Gaussian case and general theory – problems of dimensionality – components analysis and discriminants – hidden Markov models.
Nonparametric Techniques and Linear Discriminant Functions
Nonparametric techniques – density estimation – Parzen windows – nearest neighborhood estimation – rules and metrics – linear discriminant functions and decision surfaces – generalized linear discriminant functions – two-category linearly separable case – minimizing the perception criterion function.
Nonmetric methods and Algorithm-independent Machine Learning
Nonmetric methods – decision trees – CART methods – algorithm-independent machine learning – lack of inherent superiority of any classifier – bias and variance for regression and classification – resampling or estimating statistics – estimating and comparing classifiers.
Unsupervised Learning and Clustering
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 – component analysis – low-dimensional representations and multi-dimensional scaling.
Course Outcomes
CO1: To gain knowledge about pattern classification and dimensionality reduction method
CO2: To understand the use of Maximum-likelihood and Bayesian Parameter Estimation CO3: To understand and apply Nonparametric Techniques and Linear Discriminant Functions
CO4: To apply Nonmetric methods and Algorithm-independent Machine Learning CO5: To implement clustering methods under unsupervised learning
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