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Electronics - Pattern Recognition

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

Self-Study Content
  1. Mod-01 Lec-01 Introduction to Statistical Pattern Recognition

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  2. Mod-01 Lec-02 Overview of Pattern Classifiers

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  3. Mod-02 Lec-03 The Bayes Classifier for minimizing Risk

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  14. Mod-06 Lec-14 Linear Least Squares Regression; LMS algorithm

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  15. Mod-06 Lec-15 AdaLinE and LMS algorithm; General nonliner least-squares regression

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  16. Mod-06 Lec-16 Logistic Regression; Statistics of least squares method; Regularized Least Squares

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  17. Mod-06 Lec-17 Fisher Linear Discriminant

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  21. Mod-07 Lec-21 Consistency of Empirical Risk Minimization

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  25. Mod-08 Lec-25 Overview of Artificial Neural Networks

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  29. Mod-08 Lec-29 Radial Basis Function Networks; Gaussian RBF networks

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  30. Mod-08 Lec-30 Learning Weights in RBF networks; K-means clustering algorithm

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

  31. Mod-09 Lec-31 Support Vector Machines -- Introduction, obtaining the optimal hyperplane

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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

    Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IIS

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