Modules / Lectures
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Overview of Pattern classification and regressionQuestions for the whole coursePractice Problems78

Sl.No Chapter Name English
1Introduction to Statistical Pattern RecognitionDownload
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2Overview of Pattern ClassifiersDownload
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3The Bayes Classifier for minimizing RiskDownload
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4Estimating Bayes Error; Minimax and Neymann-Pearson classifiersDownload
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5Implementing Bayes Classifier; Estimation of Class Conditional DensitiesDownload
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6Maximum Likelihood estimation of different densitiesDownload
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7Bayesian estimation of parameters of density functions, MAP estimatesDownload
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8Bayesian Estimation examples; the exponential family of densities and ML estimatesDownload
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9Sufficient Statistics; Recursive formulation of ML and Bayesian estimatesDownload
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10Mixture Densities, ML estimation and EM algorithmDownload
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11Convergence of EM algorithm; overview of Nonparametric density estimationDownload
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12Nonparametric estimation, Parzen Windows, nearest neighbour methodsDownload
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13Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proofDownload
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14Linear Least Squares Regression; LMS algorithmDownload
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15AdaLinE and LMS algorithm; General nonliner least-squares regressionDownload
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16Logistic Regression; Statistics of least squares method; Regularized Least SquaresDownload
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17Fisher Linear DiscriminantDownload
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18Linear Discriminant functions for multi-class case; multi-class logistic regressionDownload
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19Learning and Generalization; PAC learning frameworkDownload
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20Overview of Statistical Learning Theory; Empirical Risk MinimizationDownload
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21Consistency of Empirical Risk MinimizationPDF unavailable
22Consistency of Empirical Risk Minimization; VC-DimensionDownload
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23Complexity of Learning problems and VC-DimensionDownload
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24VC-Dimension Examples; VC-Dimension of hyperplanesDownload
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25Overview of Artificial Neural NetworksDownload
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26Multilayer Feedforward Neural networks with Sigmoidal activation functions;Download
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27Backpropagation Algorithm; Representational abilities of feedforward networksDownload
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28Feedforward networks for Classification and Regression; Backpropagation in PracticeDownload
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29Radial Basis Function Networks; Gaussian RBF networksDownload
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30Learning Weights in RBF networks; K-means clustering algorithmDownload
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31Support Vector Machines -- Introduction, obtaining the optimal hyperplaneDownload
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32SVM formulation with slack variables; nonlinear SVM classifiersPDF unavailable
33Kernel Functions for nonlinear SVMs; Mercer and positive definite KernelsDownload
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34Support Vector Regression and ?-insensitive Loss function, examples of SVM learningDownload
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35Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizerDownload
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36Positive Definite Kernels; RKHS; Representer TheoremDownload
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37Feature Selection and Dimensionality Reduction; Principal Component AnalysisDownload
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38No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-offDownload
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39Assessing Learnt classifiers; Cross Validation;Download
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40Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoostDownload
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41Risk minimization view of AdaBoostDownload
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