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Introduction to Statistical Pattern Recognition, by P.S. Sastry

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AdaLinE and LMS Algorithm; General Nonliner least-Squares Regression, by P.S. Sastry

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Overview of Statistical Learning Theory; Empirical Risk Minimization, by P.S. Sastry

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Backpropagation Algorithm; Representational Abilities of Feedforward Networks, by P.S. Sastry

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Feedforward Networks for Classification and Regression; Backpropagation in Practice, by P.S. Sastry

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Kernel Functions for Nonlinear SVMs; Mercer and Positive Definite Kernels, by P.S. Sastry

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Bootstrap, Bagging and Boosting; Classifier Ensembles; Adaboost, by P.S. Sastry

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Learning Weights in RBF Networks; K-Means Clustering Algorithm, by P.S. Sastry

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Learning and Generalization; PAC Learning Framework, by P.S. Sastry

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Linear Discriminant Functions for Multi-Class Case; Multi-Class Logistic Regression, by P.S. Sastry

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Support Vector Regression and ?-Insensitive Loss Function, Examples of SVM Learning, by P.S. Sastry

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Bayesian Estimation of Parameters of Density Functions, MAP Estimates, by P.S. Sastry

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The Bayes Classifier for Minimizing Risk, by P.S. Sastry

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Implementing Bayes Classifier; Estimation of Class Conditional Densities, by P.S. Sastry

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Overview of Artificial Neural Networks, by P.S. Sastry

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Consistency of Empirical Risk Minimization, by P.S. Sastry

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Linear Discriminant Functions; Perceptron–Learning Algorithm and Convergence Proof, by P.S. Sastry

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Bayesian Estimation Examples; The Exponential Family of Densities and ML Estimates, by P.S. Sastry

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No Free Lunch Theorem; Model Selection and Model Estimation; Bias-Variance Trade-Off, by P.S. Sastry

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Logistic Regression; Statistics of least Squares Method; Regularized least Squares, by P.S. Sastry

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SVM Formulation with Slack Variables; Nonlinear SVM Classifiers, by P.S. Sastry

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VC-Dimension Examples; VC-Dimension of Hyperplanes, by P.S. Sastry

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Radial Basis Function Networks; Gaussian RBF Networks, by P.S. Sastry

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Risk Minimization View of AdaBoost, by P.S. Sastry

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Nonparametric Estimation, Parzen Windows, Nearest Neighbour Methods, by P.S. Sastry

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Convergence of EM Algorithm, Overview of Nonparametric Density Estimation, by P.S. Sastry

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Overview of SMO and other Algorithms for SVM; ?-SVM and ?-SVR; SVM as a Risk Minimizer, by P.S. Sastry

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Consistency of Empirical Risk Minimization; VC-Dimension, by P.S. Sastry

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Sufficient Statistics; Recursive Formulation of ML and Bayesian Estimates, by P.S. Sastry

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Support Vector Machines–Introduction, Obtaining the Optimal Hyperplane, by P.S. Sastry

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