IISc Bangalore Course , Prof. P.S. Sastry

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IISc Bangalore Course , Prof. P.S. Sastry

Contents:

Overview of Pattern classification and regression : Introduction to Statistical Pattern Recognition - Overview of Pattern Classifiers

Bayesian decision making and Bayes Classifier : The Bayes Classifier for minimizing Risk - Estimating Bayes Error; Minimax and Neymann-Pearson classifiers

Parametric Estimation of Densities : Implementing Bayes Classifier; Estimation of Class Conditional Densities - Maximum Likelihood estimation of different densities - Bayesian estimation of parameters of density functions, MAP estimates - Bayesian Estimation examples; the exponential family of densities and ML estimates - Sufficient Statistics; Recursive formulation of ML and Bayesian estimates

Mixture Densities and EM Algorithm : Mixture Densities, ML estimation and EM algorithm - Convergence of EM algorithm; overview of Nonparametric density estimation

Nonparametric density estimation : Convergence of EM algorithm; overview of Nonparametric density estimation - Nonparametric estimation, Parzen Windows, nearest neighbour methods

Linear models for classification and regression : Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof - Linear Least Squares Regression; LMS algorithm - AdaLinE and LMS algorithm; General nonliner least-squares regression - Logistic Regression; Statistics of least squares method; Regularized Least Squares - Fisher Linear Discriminant - Linear Discriminant functions for multi-class case; multi-class logistic regression

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension : Learning and Generalization; PAC learning framework - Overview of Statistical Learning Theory; Empirical Risk Minimization - Consistency of Empirical Risk Minimization - Consistency of Empirical Risk Minimization; VC-Dimension - Complexity of Learning problems and VC-Dimension - VC-Dimension Examples; VC-Dimension of hyperplanes

Artificial Neural Networks for Classification and regression : Overview of Artificial Neural Networks - Multilayer Feedforward Neural networks with Sigmoidal activation functions; - Backpropagation Algorithm; Representational abilities of feedforward networks - Feedforward networks for Classification and Regression; Backpropagation in Practice - Radial Basis Function Networks; Gaussian RBF networks - Learning Weights in RBF networks; K-means clustering algorithm

Support Vector Machines and Kernel based methods : Support Vector Machines -- Introduction, obtaining the optimal hyperplane - SVM formulation with slack variables; nonlinear SVM classifiers - Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels - Support Vector Regression and ε-insensitive Loss function, examples of SVM learning - Overview of SMO and other algorithms for SVM; ν-SVM and ν-SVR; SVM as a risk minimizer - Positive Definite Kernels; RKHS; Representer Theorem

Feature Selection, Model assessment and cross-validation : Feature Selection and Dimensionality Reduction; Principal Component Analysis - No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off - Assessing Learnt classifiers; Cross Validation;

Boosting and Classifier ensembles : Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost - Risk minimization view of AdaBoost

Overview of Pattern classification and regression : Introduction to Statistical Pattern Recognition - Overview of Pattern Classifiers

Bayesian decision making and Bayes Classifier : The Bayes Classifier for minimizing Risk - Estimating Bayes Error; Minimax and Neymann-Pearson classifiers

Parametric Estimation of Densities : Implementing Bayes Classifier; Estimation of Class Conditional Densities - Maximum Likelihood estimation of different densities - Bayesian estimation of parameters of density functions, MAP estimates - Bayesian Estimation examples; the exponential family of densities and ML estimates - Sufficient Statistics; Recursive formulation of ML and Bayesian estimates

Mixture Densities and EM Algorithm : Mixture Densities, ML estimation and EM algorithm - Convergence of EM algorithm; overview of Nonparametric density estimation

Nonparametric density estimation : Convergence of EM algorithm; overview of Nonparametric density estimation - Nonparametric estimation, Parzen Windows, nearest neighbour methods

Linear models for classification and regression : Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof - Linear Least Squares Regression; LMS algorithm - AdaLinE and LMS algorithm; General nonliner least-squares regression - Logistic Regression; Statistics of least squares method; Regularized Least Squares - Fisher Linear Discriminant - Linear Discriminant functions for multi-class case; multi-class logistic regression

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension : Learning and Generalization; PAC learning framework - Overview of Statistical Learning Theory; Empirical Risk Minimization - Consistency of Empirical Risk Minimization - Consistency of Empirical Risk Minimization; VC-Dimension - Complexity of Learning problems and VC-Dimension - VC-Dimension Examples; VC-Dimension of hyperplanes

Artificial Neural Networks for Classification and regression : Overview of Artificial Neural Networks - Multilayer Feedforward Neural networks with Sigmoidal activation functions; - Backpropagation Algorithm; Representational abilities of feedforward networks - Feedforward networks for Classification and Regression; Backpropagation in Practice - Radial Basis Function Networks; Gaussian RBF networks - Learning Weights in RBF networks; K-means clustering algorithm

Support Vector Machines and Kernel based methods : Support Vector Machines -- Introduction, obtaining the optimal hyperplane - SVM formulation with slack variables; nonlinear SVM classifiers - Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels - Support Vector Regression and ε-insensitive Loss function, examples of SVM learning - Overview of SMO and other algorithms for SVM; ν-SVM and ν-SVR; SVM as a risk minimizer - Positive Definite Kernels; RKHS; Representer Theorem

Feature Selection, Model assessment and cross-validation : Feature Selection and Dimensionality Reduction; Principal Component Analysis - No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off - Assessing Learnt classifiers; Cross Validation;

Boosting and Classifier ensembles : Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost - Risk minimization view of AdaBoost

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Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit httpnptel.ac.in

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

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