Mod-01 Lec-01 Introduction to Statistical Pattern Recognition. Mod-01 Lec-02 Overview of Pattern Classifiers. Mod-02 Lec-03 The Bayes Classifier for minimizing Risk. Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers. Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities. Mod-03 Lec-06 Maximum Likelihood estimation of different densities. Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates. Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML estimates. Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates. Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm. Mod-04 & 05 Lec-11 Convergence of EM algorithm; overview of Nonparametric density estimation. Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods. Mod-06 Lec-13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof. Mod-06 Lec-14 Linear Least Squares Regression; LMS algorithm. Mod-06 Lec-15 AdaLinE and LMS algorithm; General nonliner least-squares regression. Mod-06 Lec-16 Logistic Regression; Statistics of least squares method; Regularized Least Squares. Mod-06 Lec-17 Fisher Linear Discriminant. Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic regression. Mod-07 Lec-19 Learning and Generalization; PAC learning framework. Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization. Mod-07 Lec-21 Consistency of Empirical Risk Minimization. Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension. Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension. Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes. Mod-08 Lec-25 Overview of Artificial Neural Networks. Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;. Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward networks. Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in Practice. Mod-08 Lec-29 Radial Basis Function Networks; Gaussian RBF networks. Mod-08 Lec-30 Learning Weights in RBF networks; K-means clustering algorithm. Mod-09 Lec-31 Support Vector Machines -- Introduction, obtaining the optimal hyperplane. Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers. Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels. Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning. Mod-09 Lec-35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer. Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem. Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis. Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off. Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation;. Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost. Mod-11 Lec-41 Risk minimization view of AdaBoost.