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Lecture 37 : Regression I, by Pabitra Mitra

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Lecture 38 : Regression II, by Pabitra Mitra

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Lecture 39 : Regression III, by Pabitra Mitra

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Lecture 3Data Preprocessing-II, by Pabitra Mitra

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Lecture 4 : Types of Learning Analytics-II, by Ramkumar Rajendran

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Lecture 40 : Regression IV, by Pabitra Mitra

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Lecture 41 : Dimensionality Reduction-I, by Pabitra Mitra

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Lecture 42 : Dimensionality Reduction-II, by Pabitra Mitra

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Lecture 43 : Tutorial, by Pabitra Mitra

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Lecture 4Association Rules, by Pabitra Mitra

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Lecture 5 : Data Collection, by Ramkumar Rajendran

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Lecture 5Apriori Algorithm, by Pabitra Mitra

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Lecture 6 : Data Collection in MOOC, by Ramkumar Rajendran

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Lecture 6 : Rule Generation, by Pabitra Mitra

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Lecture 7 : Classification, by Pabitra Mitra

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Lecture 7 : Data Collection in TELE, by Ramkumar Rajendran

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Lecture 8 : Decision Tree-I, by Pabitra Mitra

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Lecture 8 : Multichannel Data, by Ramkumar Rajendran

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Lecture 9 : Decision Tree-II, by Pabitra Mitra

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Linear Discriminant Functions for Multi-Class Case; Multi-Class Logistic Regression, 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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Linear least Squares Regression; LMS Algorithm, 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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Maximum Likelihood Estimation of Different Densities, by P.S. Sastry

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Mixture Densities, ML Estimation and EM Algorithm, by P.S. Sastry

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Multilayer Feedforward Neural Networks with Sigmoidal Activation Functions;, 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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Nonparametric Estimation, Parzen Windows, Nearest Neighbour Methods, by P.S. Sastry

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

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

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Positive Definite Kernels; RKHS; Representer Theorem, 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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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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Support Vector Regression and ?-Insensitive Loss Function, Examples of SVM Learning, by P.S. Sastry

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

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

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

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