# Statistical foundations of machine learning

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## Table of contents

## Recently updated pages

Permutation test -
23 Feb 2014

Randomization tests -
23 Feb 2014

The bootstrap principle -
23 Feb 2014

Bootstrap estimate of bias -
23 Feb 2014

Bootstrap sampling -
23 Feb 2014

Bootstrap -
23 Feb 2014

Estimation of arbitrary statistics -
23 Feb 2014

Nonparametric methods -
23 Feb 2014

Receiver Operating Characteristic curve -
23 Feb 2014

A posteriori assessment of a test -
02 Feb 2014

## Statistics : Total views

Statistical foundations of machine learning - 8,289 | The random model of uncertainty - 1,455 |

Introduction - 3,326 | Plug-in principle to define an estimator - 1,431 |

Modelling from data - 2,996 | Stacked regression - 1,412 |

The sum of i.i.d. random variables - 2,269 | Outline - 1,379 |

USPS dataset - 2,154 | Bias/variance decomposition of MSE - 1,370 |

Foundations of probability - 2,075 | Classical parametric estimation - 1,361 |

Stastistical machine learning - 1,930 | Classification and Regression Trees - 1,340 |

Bias and variance of $\hat \mu$ - 1,742 | Bivariate normal distribution - 1,327 |

Bias of the estimator $\hat \sigma^2$ - 1,598 | Bias and variance - 1,324 |

Axiomatic definition of probability - 1,548 | Normal distribution: the scalar case - 1,323 |

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