Prof. P.S. Sastry, Department of Electronics and Communication Engineering, IISc Bangalore.
This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier; parametric estimation of densities; mixture densities and EM algorithm; Nonparametric Density Estimation; Linear Models for Classification and Regression; overview of statistical learning theory; empirical risk minimization and VC-dimension; artificial neural networks for classification and regression; support vector machines and kernel-based methods; feature selection, model assessment and cross-validation; boosting and classifier ensembles.