Modules / Lectures
Module NameDownloadDescriptionDownload Size
Overview of Pattern classification and regressionLecture 1Lecture Notes267 kb
Overview of Pattern classification and regressionLecture 2Lecture Notes413 kb
Bayesian decision making and Bayes ClassifierLecture 3Lecture Notes238 kb
Bayesian decision making and Bayes ClassifierLecture 4Lecture Notes342 kb
Parametric Estimation of DensitiesLecture 5Lecture Notes331 kb
Parametric Estimation of DensitiesLecture 6Lecture Notes302 kb
Parametric Estimation of DensitiesLecture 7Lecture Notes292 kb
Parametric Estimation of DensitiesLecture 8Lecture Notes271 kb
Parametric Estimation of DensitiesLecture 9Lecture Notes338 kb
Mixture Densities and EM AlgorithmLecture 10Lecture Notes551 kb
Mixture Densities and EM AlgorithmLecture 11Lecture Notes718 kb
Nonparametric density estimationLecture 11Lecture Notes718 kb
Nonparametric density estimationLecture 12Lecture Notes349 kb
Linear models for classification and regressionLecture 13Lecture Notes403 kb
Linear models for classification and regressionLecture 14Lecture Notes336 kb
Linear models for classification and regressionLecture 15Lecture Notes384 kb
Linear models for classification and regressionLecture 16Lecture Notes301 kb
Linear models for classification and regressionLecture 17Lecture Notes339 kb
Linear models for classification and regressionLecture 18Lecture Notes281 kb
Overview of statistical learning theory, Empirical Risk Minimization and VC-DimensionLecture 19Lecture Notes424 kb
Overview of statistical learning theory, Empirical Risk Minimization and VC-DimensionLecture 20Lecture Notes401 kb
Overview of statistical learning theory, Empirical Risk Minimization and VC-DimensionLecture 21Lecture Notes348 kb
Overview of statistical learning theory, Empirical Risk Minimization and VC-DimensionLecture 22Lecture Notes277 kb
Overview of statistical learning theory, Empirical Risk Minimization and VC-DimensionLecture 23Lecture Notes304 kb
Overview of statistical learning theory, Empirical Risk Minimization and VC-DimensionLecture 24Lecture Notes346 kb
Artificial Neural Networks for Classification and regressionLecture 25Lecture Notes426 kb
Artificial Neural Networks for Classification and regressionLecture 26Lecture Notes446 kb
Artificial Neural Networks for Classification and regressionLecture 27Lecture Notes319 kb
Artificial Neural Networks for Classification and regressionLecture 28Lecture Notes381 kb
Artificial Neural Networks for Classification and regressionLecture 29Lecture Notes284 kb
Artificial Neural Networks for Classification and regressionLecture 30Lecture Notes394 kb
Support Vector Machines and Kernel based methodsLecture 31Lecture Notes404 kb
Support Vector Machines and Kernel based methodsLecture 32Lecture Notes412 kb
Support Vector Machines and Kernel based methodsLecture 33Lecture Notes451 kb
Support Vector Machines and Kernel based methodsLecture 34Lecture Notes395 kb
Support Vector Machines and Kernel based methodsLecture 35Lecture Notes575 kb
Support Vector Machines and Kernel based methodsLecture 36Lecture Notes358 kb
Feature Selection, Model assessment and cross-validationLecture 37Lecture Notes369 kb
Feature Selection, Model assessment and cross-validationLecture 38Lecture Notes384 kb
Feature Selection, Model assessment and cross-validationLecture 39Lecture Notes330 kb
Boosting and Classifier ensemblesLecture 40Lecture Notes359 kb
Boosting and Classifier ensemblesLecture 41Lecture Notes329 kb
Module NameDownload
Module NameDownloadDescriptionDownload Size
Overview of Pattern classification and regressionQuestions for the whole coursePractice Problems78 kb

Sl.No Chapter Name English
1Introduction to Statistical Pattern RecognitionPDF unavailable
2Overview of Pattern ClassifiersPDF unavailable
3The Bayes Classifier for minimizing RiskPDF unavailable
4Estimating Bayes Error; Minimax and Neymann-Pearson classifiersPDF unavailable
5Implementing Bayes Classifier; Estimation of Class Conditional DensitiesPDF unavailable
6Maximum Likelihood estimation of different densitiesPDF unavailable
7Bayesian estimation of parameters of density functions, MAP estimatesPDF unavailable
8Bayesian Estimation examples; the exponential family of densities and ML estimatesPDF unavailable
9Sufficient Statistics; Recursive formulation of ML and Bayesian estimatesPDF unavailable
10Mixture Densities, ML estimation and EM algorithmPDF unavailable
11Convergence of EM algorithm; overview of Nonparametric density estimationPDF unavailable
12Convergence of EM algorithm, Overview of Nonparametric density estimationPDF unavailable
13Nonparametric estimation, Parzen Windows, nearest neighbour methodsPDF unavailable
14Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proofPDF unavailable
15Linear Least Squares Regression; LMS algorithmPDF unavailable
16AdaLinE and LMS algorithm; General nonliner least-squares regressionPDF unavailable
17Logistic Regression; Statistics of least squares method; Regularized Least SquaresPDF unavailable
18Fisher Linear DiscriminantPDF unavailable
19Linear Discriminant functions for multi-class case; multi-class logistic regressionPDF unavailable
20Learning and Generalization; PAC learning frameworkPDF unavailable
21Overview of Statistical Learning Theory; Empirical Risk MinimizationPDF unavailable
22Consistency of Empirical Risk MinimizationPDF unavailable
23Consistency of Empirical Risk Minimization; VC-DimensionPDF unavailable
24Complexity of Learning problems and VC-DimensionPDF unavailable
25VC-Dimension Examples; VC-Dimension of hyperplanesPDF unavailable
26Overview of Artificial Neural NetworksPDF unavailable
27Multilayer Feedforward Neural networks with Sigmoidal activation functions;PDF unavailable
28Backpropagation Algorithm; Representational abilities of feedforward networksPDF unavailable
29Feedforward networks for Classification and Regression; Backpropagation in PracticePDF unavailable
30Radial Basis Function Networks; Gaussian RBF networksPDF unavailable
31Learning Weights in RBF networks; K-means clustering algorithmPDF unavailable
32Support Vector Machines -- Introduction, obtaining the optimal hyperplanePDF unavailable
33SVM formulation with slack variables; nonlinear SVM classifiersPDF unavailable
34Kernel Functions for nonlinear SVMs; Mercer and positive definite KernelsPDF unavailable
35Support Vector Regression and ?-insensitive Loss function, examples of SVM learningPDF unavailable
36Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizerPDF unavailable
37Positive Definite Kernels; RKHS; Representer TheoremPDF unavailable
38Feature Selection and Dimensionality Reduction; Principal Component AnalysisPDF unavailable
39No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-offPDF unavailable
40Assessing Learnt classifiers; Cross Validation;PDF unavailable
41Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoostPDF unavailable
42Risk minimization view of AdaBoostPDF unavailable


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