# 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

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Statistical foundations of machine learning - 7,872 | The random model of uncertainty - 1,389 |

Introduction - 3,188 | Stacked regression - 1,317 |

Modelling from data - 2,873 | Outline - 1,297 |

Foundations of probability - 1,987 | Plug-in principle to define an estimator - 1,297 |

The sum of i.i.d. random variables - 1,958 | Classical parametric estimation - 1,276 |

USPS dataset - 1,934 | Classification and Regression Trees - 1,254 |

Stastistical machine learning - 1,818 | Normal distribution: the scalar case - 1,246 |

Bias and variance of $\hat \mu$ - 1,605 | Bivariate normal distribution - 1,217 |

Bias of the estimator $\hat \sigma^2$ - 1,479 | Sample average - 1,189 |

Axiomatic definition of probability - 1,465 | Bias and variance - 1,160 |

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