Lecture 1 | Jan 9 | Introduction to probability theory |

See also, Statistics for Nuclear and Particle Phycicists by Louis Lyons | ||

as well as Practical Statistics for Particle Physicists by Luca Lista (INFN) - arXiv:1609.04150v2 | ||

Lecture 2 | Jan 11 | Statistics and parameter estimation |

Lecture 3 | Jan 16 | Progressive fit |

Lecture 4 | Jan 18 | Constrained minimization |

Lecture 5 | Jan 23 | Likelihood functions and confidence intervals |

Lecture 6 | Jan 25 | Binned and unbinned likelihood fits |

Lecture 7 | Jan 30 | Goodness of fit, Monte Carlo methods |

See also, Monte Carlo Techniques - Particle Data Group | ||

Lecture 8 | Feb 1 | Template methods and unfolding |

Lecture 9 | Feb 6 | Tikhonov regularization, d'Agostini iteration |

See also, TUnfold note, and user's manual. | ||

Also, a useful set of slides... | ||

Lecture 10 | Feb 8 | Multivariate methods |

Lecture 11 | Feb 13 | Artificial neural networks, support vector machines |

Lecture 12 | Feb 15 | Parton distribution functions |

Lecture 13 | Feb 20 | More parton distribution functions |

Lecture 14 | Feb 22 | |

Lecture 15 | Feb 27 | |

Lecture 16 | Mar 1 | |

Lecture 17 | Mar 6 | |

Lecture 18 | Mar 8 | |

Lecture 19 | Mar 20 | |

Lecture 20 | Mar 22 | |

Lecture 21 | Mar 27 | |

Lecture 22 | Mar 29 | |

Lecture 23 | Apr 3 | |

Lecture 24 | Apr 5 | |

Lecture 25 | Apr 9 | |

Lecture 26 | Apr 12 | |

Lecture 27 | Apr 17 | |

Lecture 28 | Apr 19 | |

Lecture 29 | Apr 24 | |

Lecture 30 | Apr 26 |