# Marloes H. Maathuis: Catalogue data in Autumn Semester 2016

Name | Prof. Dr. Marloes H. Maathuis |

Field | Statistics |

Address | Seminar für Statistik (SfS) ETH Zürich, HG G 15.1 Rämistrasse 101 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 61 84 |

marloes.maathuis@stat.math.ethz.ch | |

URL | http://stat.ethz.ch/~maathuis |

Department | Mathematics |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

401-0603-00L | Stochastics (Probability and Statistics) | 4 credits | 2V + 1U | M. H. Maathuis | |

Abstract | This class covers the following concepts: random variables, probability, discrete and continuous distributions, joint and conditional probabilities and distributions, the law of large numbers, the central limit theorem, descriptive statistics, statistical inference, inference for normally distributed data, point estimation, and two-sample tests. | ||||

Objective | Knowledge of the basic principles of probability and statistics. | ||||

Content | Introduction to probability theory, some basic principles from mathematical statistics and basic methods for applied statistics. | ||||

Lecture notes | Lecture notes | ||||

Literature | Lecture notes | ||||

401-3611-00L | Advanced Topics in Computational StatisticsDoes not take place this semester. | 4 credits | 2V | M. H. Maathuis | |

Abstract | This lecture covers selected advanced topics in computational statistics, including various classification methods, the EM algorithm, clustering, handling missing data, and graphical modelling. | ||||

Objective | Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes. | ||||

Content | The course is roughly divided in three parts: (1) Supervised learning via (variations of) nearest neighbor methods, (2) the EM algorithm and clustering, (3) handling missing data and graphical models. | ||||

Lecture notes | Lecture notes. | ||||

Prerequisites / Notice | We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics. | ||||

401-5620-00L | Research Seminar on Statistics | 0 credits | 2K | P. L. Bühlmann, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, M. Wolf | |

Abstract | Research colloquium | ||||

Objective | |||||

401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl | |

Abstract | About 5 talks on applied statistics. | ||||

Objective | See how statistical methods are applied in practice. | ||||

Content | There will be about 5 talks on how statistical methods are applied in practice. | ||||

Prerequisites / Notice | This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker. |