Electronics - Pattern Recognition by P. S. Sastry (IISc Bangalore)

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source: nptelhrd     2013年12月1日
Electronics - Pattern Recognition by Prof. P. S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visithttp://nptel.ac.in

01 Introduction to Statistical Pattern Recognition 55:00
02 Overview of Pattern Classifiers 55:39
03 The Bayes Classifier for minimizing Risk 56:41
04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers 57:16
05 Implementing Bayes Classifier; Estimation of Class Conditional Densities 58:08
06 Maximum Likelihood estimation of different densities 58:16
07 Bayesian estimation of parameters of density functions, MAP estimates 57:06
08 Bayesian Estimation examples; the exponential family of densities and ML estimates 57:05
09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates 58:07
10 Mixture Densities, ML estimation and EM algorithm 57:27
11 Convergence of EM algorithm; overview of Nonparametric density estimation 58:18
12 Nonparametric estimation, Parzen Windows, nearest neighbour methods 57:30
13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof 58:22
14 Linear Least Squares Regression; LMS algorithm 58:16
15 AdaLinE and LMS algorithm; General nonliner least-squares regression 58:18
16 Logistic Regression; Statistics of least squares method; Regularized Least Squares 58:23
17 Fisher Linear Discriminant 58:12
18 Linear Discriminant functions for multi-class case; multi-class logistic regression 57:24
19 Learning and Generalization; PAC learning framework 59:02
20 Overview of Statistical Learning Theory; Empirical Risk Minimization 58:53
21 Consistency of Empirical Risk Minimization 58:35
22 Consistency of Empirical Risk Minimization; VC-Dimension 58:14
23 Complexity of Learning problems and VC-Dimension 58:38
24 VC-Dimension Examples; VC-Dimension of hyperplanes 59:00
25 Overview of Artificial Neural Networks 59:11
26 Multilayer Feedforward Neural networks with Sigmoidal activation functions; 58:57
27 Backpropagation Algorithm; Representational abilities of feedforward networks 59:01
28 Feedforward networks for Classification and Regression; Backpropagation in Practice 58:40
29 Radial Basis Function Networks; Gaussian RBF networks 58:04
30 Learning Weights in RBF networks; K-means clustering algorithm 59:02
31 Support Vector Machines -- Introduction, obtaining the optimal hyperplane 58:54
32 SVM formulation with slack variables; nonlinear SVM classifiers 59:00
33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels 58:45
34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning 58:40
35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer 58:29
36 Positive Definite Kernels; RKHS; Representer Theorem 58:46
37 Feature Selection and Dimensionality Reduction; Principal Component Analysis 59:14
38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off 59:53
39 Assessing Learnt classifiers; Cross Validation; 59:50
40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost 59:31
41 Risk minimization view of AdaBoost 58:52