IISc Bangalore Course , Prof. P.S. Sastry

**291**students enrolled

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

Up Next

You can skip ad in

SKIP AD >

Advertisement

- 2x
- 1.5x
- 1x
- 0.5x
- 0.25x

EMBED LINK

COPY

DIRECT LINK

COPY

PRIVATE CONTENT

OK

Enter password to view

Please enter valid password!

- Play Pause
- Mute UnMute
- Fullscreen Normal
- @Your Company Title

0:00

0 (0 Ratings)

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

Heads up!

These lecture videos are delivered by IISc Bangalore, under the NPTEL program, lot of nptel video courses are available for learning online.
0%

0%

0%

0%

0%

- 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

- FreeVideoLectures aim to help millions of students across the world acquire knowledge, gain good grades, get jobs, assist in getting promotions through quality learning material.

- You can write to us
- help@freevideolectures.com

2018 FreeVideoLectures. All rights reserved. FreeVideoLectures only promotes free course material from different sources, we are not endrosed by any university.