Nicolai Meinshausen: Katalogdaten im Herbstsemester 2017 |
| Name | Herr Prof. Dr. Nicolai Meinshausen |
| Lehrgebiet | Statistik |
| Adresse | Professur für Statistik ETH Zürich, HG G 23.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 32 74 |
| meinshausen@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~nicolai |
| Departement | Mathematik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 401-3620-67L | Student Seminar in Statistics: Computer Age Statistical Inference  Maximale Teilnehmerzahl: 24 Hauptsächlich für Studierende im Studiengang Mathematik Bachelor oder Master, welche zusätzlich zum Einführungskurs 401-2604-00L Wahrscheinlichkeit und Statistik / Probability and Statistics mindestens ein Kern- oder Wahlfach in Statistik besucht haben. | 4 KP | 2S | M. H. Maathuis, P. L. Bühlmann, N. Meinshausen, S. van de Geer | |
| Kurzbeschreibung | We study selected chapters from the book "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie. | ||||
| Lernziel | During this seminar, we will study roughly one chapter per week from the book "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie. You will obtain a good overview of the field of modern statistics. Moreover, you will practice your self-studying and presentation skills. | ||||
| Inhalt | In the words of Efron and Hastie: "The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data,” “data science,” and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on a journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories – Bayesian, frequentist, Fisherian – individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The book integrates methodology and algorithms with statistical inference, and ends with speculation on the future direction of statistics and data science." | ||||
| Literatur | Bradley Efron and Trevor Hastie (2016). Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press, New York. ISBN: 9781107149892. | ||||
| Voraussetzungen / Besonderes | We require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics, as well as some experience with the statistical software R. Topics will be assigned during the first meeting. | ||||
| 401-4619-67L | Advanced Topics in Computational Statistics | 4 KP | 2V | N. Meinshausen | |
| Kurzbeschreibung | This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling. | ||||
| Lernziel | Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes. | ||||
| Inhalt | The main focus will be on graphical models in various forms: Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models | ||||
| Voraussetzungen / Besonderes | 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 KP | 2K | L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf | |
| Kurzbeschreibung | Research colloquium | ||||
| Lernziel | |||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 KP | 1K | M. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |
| Kurzbeschreibung | Etwa 5 Vorträge zur angewandten Statistik. | ||||
| Lernziel | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten. | ||||
| Inhalt | In etwa 5 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. | ||||
| Voraussetzungen / Besonderes | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm: http://stat.ethz.ch/events/zukost Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. | ||||