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Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in Practice

Mod-06 Lec-13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof

Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost

Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods

Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation;

Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward networks

Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;

Mod-08 Lec-25 Overview of Artificial Neural Networks

Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off

Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes

Mod-04 & 05 Lec-11 Convergence of EM algorithm; overview of Nonparametric density estimation

Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis

Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension

Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm

Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem

Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates

Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension

Mod-09 Lec-35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer

Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML estimates

Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning

Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates

Mod-07 Lec-21 Consistency of Empirical Risk Minimization

Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization

Mod-07 Lec-19 Learning and Generalization; PAC learning framework

Mod-03 Lec-06 Maximum Likelihood estimation of different densities

Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels

Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities

Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic regression

Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers

Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers

Description:

Prof. P.S. Sastry, Department of Electronics and Communication Engineering, IISc Bangalore. This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier; parametric estimation of densities; mixture densities and EM algorithm; Nonparametric Density Estimation; Linear Models for Classification and Regression; overview of statistical learning theory; empirical risk minimization and VC-dimension; artificial neural networks for classification and regression; support vector machines and kernel-based methods; feature selection, model assessment and cross-validation; boosting and classifier ensembles.

Pattern Recognition

NPTEL

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#Computer Science #Machine Learning #Artificial Intelligence #Neural Networks #Feature Selection #Model Selection #Pattern Recognition #Ensemble Methods #Backpropagation

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