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.
