Modules / Lectures

Module Name | Download | Description | Download Size |
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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 |