Mod-01 Lec-01 Introduction to Statistical Pattern Recognition Electronics Engineering 55 min Click to view videos

Video Lecture Description Sub-Category Time Click to view video Back to Engineering Video Lecture Course Page Mod-01 Lec-01 Introduction to Statistical Pattern Recognition Electronics Engineering 55 min Click to view videos Mod-01 Lec-02 Overview of Pattern Classifiers Electronics Engineering 56 min Click to view videos Mod-02 Lec-03 The Bayes Classifier for minimizing Risk Electronics Engineering 57 min Click to view videos Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers Electronics Engineering 57 min Click to view videos Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities Electronics Engineering 58 min Click to view videos Mod-03 Lec-06 Maximum Likelihood estimation of different densities Electronics Engineering 58 min Click to view videos Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates Electronics Engineering 57 min Click to view videos Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML est Electronics Engineering 57 min Click to view videos Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates Electronics Engineering 58 min Click to view videos Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm Electronics Engineering 57 min Click to view videos Mod-04 Lec-11 Convergence of EM algorithm; overview of Nonparametric density estimation Electronics Engineering 58 min Click to view videos Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods Electronics Engineering 58 min Click to view videos Mod-06 Lec-13 Linear Discriminant Functions; Perceptron — Learning Algorithm and converge Electronics Engineering 58 min Click to view videos Mod-06 Lec-14 Linear Least Squares Regression; LMS algorithm Electronics Engineering 58 min Click to view videos Mod-06 Lec-15 AdaLinE and LMS algorithm; General nonliner least-squares regression Electronics Engineering 58 min Click to view videos Mod-06 Lec-16 Logistic Regression; Statistics of least squares method; Regularized Least S Electronics Engineering 58 min Click to view videos Mod-06 Lec-17 Fisher Linear Discriminant Electronics Engineering 58 min Click to view videos Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic reg Electronics Engineering 57 min Click to view videos Mod-07 Lec-19 Learning and Generalization; PAC learning framework Electronics Engineering 59 min Click to view videos Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization Electronics Engineering 59 min Click to view videos Mod-07 Lec-21 Consistency of Empirical Risk Minimization Electronics Engineering 59 min Click to view videos Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension Electronics Engineering 58 min Click to view videos Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension Electronics Engineering 59 min Click to view videos Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes Electronics Engineering 59 min Click to view videos Mod-08 Lec-25 Overview of Artificial Neural Networks Electronics Engineering 59 min Click to view videos Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions; Electronics Engineering 59 min Click to view videos Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward network Electronics Engineering 59 min Click to view videos Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in P Electronics Engineering 59 min Click to view videos Mod-08 Lec-29 Radial Basis Function Networks; Gaussian RBF networks Electronics Engineering 58 min Click to view videos Mod-08 Lec-30 Learning Weights in RBF networks; K-means clustering algorithm Electronics Engineering 59 min Click to view videos Mod-09 Lec-31 Support Vector Machines — Introduction, obtaining the optimal hyperplane Electronics Engineering 59 min Click to view videos Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers Electronics Engineering 59 min Click to view videos Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels Electronics Engineering 59 min Click to view videos Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM l Electronics Engineering 59 min Click to view videos Mod-09 Lec-35 Overview of SMO and other algorithms for SVM Electronics Engineering 58 min Click to view videos Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem Electronics Engineering 59 min Click to view videos Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis Electronics Engineering 59 min Click to view videos Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance t Electronics Engineering 60 min Click to view videos Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation; Electronics Engineering 60 min Click to view videos Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost Electronics Engineering 60 min Click to view videos Mod-11 Lec-41 Risk minimization view of AdaBoost Electronics Engineering 59 min Click to view videos