Martin Mächler: Catalogue data in Spring Semester 2015 |
| Name | Prof. em. Dr. Martin Mächler |
| Name variants | Martin Maechler |
| Address | Seminar für Statistik (SfS) ETH Zürich, HG GO 14.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 34 08 |
| maechler@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~maechler |
| Department | Mathematics |
| Relationship | Retired Adjunct Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-3632-00L | Computational Statistics  | 10 credits | 3V + 2U | M. Mächler, P. L. Bühlmann | |
| Abstract | "Computational Statistics" deals with modern methods of data analysis (aka "data science") for prediction and inference. An overview of existing methodology is provided and also by the exercises, the student is taught to choose among possible models and about their algorithms and to validate them using graphical methods and simulation based approaches. | ||||
| Learning objective | Getting to know modern methods of data analysis for prediction and inference. Learn to choose among possible models and about their algorithms. Validate them using graphical methods and simulation based approaches. | ||||
| Content | Course Synopsis: multiple regression, nonparametric methods for regression and classification (kernel estimates, smoothing splines, regression and classification trees, additive models, projection pursuit, neural nets, ridging and the lasso, boosting). Problems of interpretation, reliable prediction and the curse of dimensionality are dealt with using resampling, bootstrap and cross validation. Details are available via http://stat.ethz.ch/education/ . Exercises will be based on the open-source statistics software R (http://www.R-project.org/). Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended. More details are available via the webpage http://stat.ethz.ch/education/ (-> "Computational Statistics"). | ||||
| Lecture notes | lecture notes are available online; see http://stat.ethz.ch/education/ (-> "Computational Statistics"). | ||||
| Literature | (see the link above, and the lecture notes) | ||||
| Prerequisites / Notice | Basic "applied" mathematical calculus and linear algebra. At least one semester of (basic) probability and statistics. | ||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, P. L. Bühlmann, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |
| Abstract | 5 to 6 talks on applied statistics. | ||||
| Learning objective | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin. | ||||
| Content | In 5-6 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. 3 bis 4 der Vorträge stehen in der Regel unter einem Semesterthema. | ||||
| Lecture notes | Bei manchen Vorträgen werden Unterlagen verteilt. Eine Zusammenfassung ist kurz vor den Vorträgen im Internet unter http://stat.ethz.ch/talks/zukost abrufbar. Ankündigunen der Vorträge werden auf Wunsch zugesandt. | ||||
| Prerequisites / Notice | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm. Koordinator M. Kalisch, Tel. 044 632 3435 Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. Course language is English or German and may depend on the speaker. | ||||
| 401-6228-00L | Programming with R for Reproducible Research | 1 credit | 1G | M. Mächler | |
| Abstract | Deeper understanding of R: Function calls, rather than "commands". Reproducible research and data analysis via Sweave and Rmarkdown. Limits of floating point arithmetic. Understanding how functions work. Environments, packages, namespaces. Closures, i.e., Functions returning functions. Lists and [mc]lapply() for easy parallelization. Performance measurement and improvements. | ||||
| Learning objective | |||||
| Content | See https://stat.ethz.ch/education/semesters/ss2014/Progr_R3 | ||||
| Lecture notes | Material available from https://stat.ethz.ch/education/semesters/ss2014/Progr_R3 | ||||
| Literature | Norman Matloff (2011) The Art of R Programming - A tour of statistical software design. no starch press, San Francisco. on stock at Polybuchhandlung (CHF 42.-). | ||||
| Prerequisites / Notice | R Knowledge on the same level as after *both* parts of the ETH lecture 401-6217-00L Using R for Data Analysis and Graphics http://www.vvz.ethz.ch/Vorlesungsverzeichnis/lerneinheitPre.do?semkez=2013W&lerneinheitId=84563&ansicht=ALLE&lang=de | ||||