Modules / Lectures

Module Name | Download | Description | Download Size |
---|---|---|---|

Overview of Pattern classification and regression | Lecture 1 | Lecture Notes | 267 kb |

Overview of Pattern classification and regression | Lecture 2 | Lecture Notes | 413 kb |

Bayesian decision making and Bayes Classifier | Lecture 3 | Lecture Notes | 238 kb |

Bayesian decision making and Bayes Classifier | Lecture 4 | Lecture Notes | 342 kb |

Parametric Estimation of Densities | Lecture 5 | Lecture Notes | 331 kb |

Parametric Estimation of Densities | Lecture 6 | Lecture Notes | 302 kb |

Parametric Estimation of Densities | Lecture 7 | Lecture Notes | 292 kb |

Parametric Estimation of Densities | Lecture 8 | Lecture Notes | 271 kb |

Parametric Estimation of Densities | Lecture 9 | Lecture Notes | 338 kb |

Mixture Densities and EM Algorithm | Lecture 10 | Lecture Notes | 551 kb |

Mixture Densities and EM Algorithm | Lecture 11 | Lecture Notes | 718 kb |

Nonparametric density estimation | Lecture 11 | Lecture Notes | 718 kb |

Nonparametric density estimation | Lecture 12 | Lecture Notes | 349 kb |

Linear models for classification and regression | Lecture 13 | Lecture Notes | 403 kb |

Linear models for classification and regression | Lecture 14 | Lecture Notes | 336 kb |

Linear models for classification and regression | Lecture 15 | Lecture Notes | 384 kb |

Linear models for classification and regression | Lecture 16 | Lecture Notes | 301 kb |

Linear models for classification and regression | Lecture 17 | Lecture Notes | 339 kb |

Linear models for classification and regression | Lecture 18 | Lecture Notes | 281 kb |

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension | Lecture 19 | Lecture Notes | 424 kb |

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension | Lecture 20 | Lecture Notes | 401 kb |

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension | Lecture 21 | Lecture Notes | 348 kb |

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension | Lecture 22 | Lecture Notes | 277 kb |

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension | Lecture 23 | Lecture Notes | 304 kb |

Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension | Lecture 24 | Lecture Notes | 346 kb |

Artificial Neural Networks for Classification and regression | Lecture 25 | Lecture Notes | 426 kb |

Artificial Neural Networks for Classification and regression | Lecture 26 | Lecture Notes | 446 kb |

Artificial Neural Networks for Classification and regression | Lecture 27 | Lecture Notes | 319 kb |

Artificial Neural Networks for Classification and regression | Lecture 28 | Lecture Notes | 381 kb |

Artificial Neural Networks for Classification and regression | Lecture 29 | Lecture Notes | 284 kb |

Artificial Neural Networks for Classification and regression | Lecture 30 | Lecture Notes | 394 kb |

Support Vector Machines and Kernel based methods | Lecture 31 | Lecture Notes | 404 kb |

Support Vector Machines and Kernel based methods | Lecture 32 | Lecture Notes | 412 kb |

Support Vector Machines and Kernel based methods | Lecture 33 | Lecture Notes | 451 kb |

Support Vector Machines and Kernel based methods | Lecture 34 | Lecture Notes | 395 kb |

Support Vector Machines and Kernel based methods | Lecture 35 | Lecture Notes | 575 kb |

Support Vector Machines and Kernel based methods | Lecture 36 | Lecture Notes | 358 kb |

Feature Selection, Model assessment and cross-validation | Lecture 37 | Lecture Notes | 369 kb |

Feature Selection, Model assessment and cross-validation | Lecture 38 | Lecture Notes | 384 kb |

Feature Selection, Model assessment and cross-validation | Lecture 39 | Lecture Notes | 330 kb |

Boosting and Classifier ensembles | Lecture 40 | Lecture Notes | 359 kb |

Boosting and Classifier ensembles | Lecture 41 | Lecture Notes | 329 kb |

Module Name | Download |
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Module Name | Download | Description | Download Size |
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Overview of Pattern classification and regression | Questions for the whole course | Practice Problems | 78 kb |

Sl.No | Chapter Name | English |
---|---|---|

1 | Introduction to Statistical Pattern Recognition | PDF unavailable |

2 | Overview of Pattern Classifiers | PDF unavailable |

3 | The Bayes Classifier for minimizing Risk | PDF unavailable |

4 | Estimating Bayes Error; Minimax and Neymann-Pearson classifiers | PDF unavailable |

5 | Implementing Bayes Classifier; Estimation of Class Conditional Densities | PDF unavailable |

6 | Maximum Likelihood estimation of different densities | PDF unavailable |

7 | Bayesian estimation of parameters of density functions, MAP estimates | PDF unavailable |

8 | Bayesian Estimation examples; the exponential family of densities and ML estimates | PDF unavailable |

9 | Sufficient Statistics; Recursive formulation of ML and Bayesian estimates | PDF unavailable |

10 | Mixture Densities, ML estimation and EM algorithm | PDF unavailable |

11 | Convergence of EM algorithm; overview of Nonparametric density estimation | PDF unavailable |

12 | Convergence of EM algorithm, Overview of Nonparametric density estimation | PDF unavailable |

13 | Nonparametric estimation, Parzen Windows, nearest neighbour methods | PDF unavailable |

14 | Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof | PDF unavailable |

15 | Linear Least Squares Regression; LMS algorithm | PDF unavailable |

16 | AdaLinE and LMS algorithm; General nonliner least-squares regression | PDF unavailable |

17 | Logistic Regression; Statistics of least squares method; Regularized Least Squares | PDF unavailable |

18 | Fisher Linear Discriminant | PDF unavailable |

19 | Linear Discriminant functions for multi-class case; multi-class logistic regression | PDF unavailable |

20 | Learning and Generalization; PAC learning framework | PDF unavailable |

21 | Overview of Statistical Learning Theory; Empirical Risk Minimization | PDF unavailable |

22 | Consistency of Empirical Risk Minimization | PDF unavailable |

23 | Consistency of Empirical Risk Minimization; VC-Dimension | PDF unavailable |

24 | Complexity of Learning problems and VC-Dimension | PDF unavailable |

25 | VC-Dimension Examples; VC-Dimension of hyperplanes | PDF unavailable |

26 | Overview of Artificial Neural Networks | PDF unavailable |

27 | Multilayer Feedforward Neural networks with Sigmoidal activation functions; | PDF unavailable |

28 | Backpropagation Algorithm; Representational abilities of feedforward networks | PDF unavailable |

29 | Feedforward networks for Classification and Regression; Backpropagation in Practice | PDF unavailable |

30 | Radial Basis Function Networks; Gaussian RBF networks | PDF unavailable |

31 | Learning Weights in RBF networks; K-means clustering algorithm | PDF unavailable |

32 | Support Vector Machines -- Introduction, obtaining the optimal hyperplane | PDF unavailable |

33 | SVM formulation with slack variables; nonlinear SVM classifiers | PDF unavailable |

34 | Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels | PDF unavailable |

35 | Support Vector Regression and ?-insensitive Loss function, examples of SVM learning | PDF unavailable |

36 | Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer | PDF unavailable |

37 | Positive Definite Kernels; RKHS; Representer Theorem | PDF unavailable |

38 | Feature Selection and Dimensionality Reduction; Principal Component Analysis | PDF unavailable |

39 | No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off | PDF unavailable |

40 | Assessing Learnt classifiers; Cross Validation; | PDF unavailable |

41 | Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost | PDF unavailable |

42 | Risk minimization view of AdaBoost | PDF unavailable |

Sl.No | Language | Book link |
---|---|---|

1 | English | Not Available |

2 | Bengali | Not Available |

3 | Gujarati | Not Available |

4 | Hindi | Not Available |

5 | Kannada | Not Available |

6 | Malayalam | Not Available |

7 | Marathi | Not Available |

8 | Tamil | Not Available |

9 | Telugu | Not Available |