# 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 - 9,591 | Axiomatic definition of probability - 1,970 |

Introduction - 3,774 | Plug-in principle to define an estimator - 1,822 |

Modelling from data - 3,451 | Bivariate normal distribution - 1,781 |

The sum of i.i.d. random variables - 2,997 | The random model of uncertainty - 1,773 |

USPS dataset - 2,863 | Stacked regression - 1,754 |

Foundations of probability - 2,479 | Outline - 1,685 |

Stastistical machine learning - 2,287 | Bias and variance - 1,671 |

Bias and variance of $\hat \mu$ - 2,277 | Normal distribution: the scalar case - 1,658 |

Bias/variance decomposition of MSE - 2,012 | Classification and Regression Trees - 1,651 |

Bias of the estimator $\hat \sigma^2$ - 1,997 | Classical parametric estimation - 1,615 |

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