Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Martin Mächler: Catalogue data in Spring Semester 2017

Name Prof. Dr. Martin Mächler
Address
Seminar für Statistik (SfS)
ETH Zürich, HG G 16
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 34 08
E-mailmaechler@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~maechler
DepartmentMathematics
RelationshipAdjunct Professor

NumberTitleECTSHoursLecturers
401-3632-00LComputational Statistics Information 10 credits3V + 2UM. 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.
ObjectiveGetting 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.
ContentCourse 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 https://stat.ethz.ch/lectures/ .

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 https://stat.ethz.ch/lectures/ (-> "Computational Statistics").
Lecture noteslecture notes are available online; see
http://stat.ethz.ch/lectures/ (-> "Computational Statistics").
Literature(see the link above, and the lecture notes)
Prerequisites / NoticeBasic "applied" mathematical calculus (incl. simple two-dimensional) and linear algebra (including Eigenvalue decomposition) similar to two semester "Analysis" in an ETH (math or) engineer's bachelor.

At least one semester of (basic) probability and statistics, as e.g., taught in an ETH engineer's or math bachelor.

Programming experience in either a compiler-based computer language (such as C++) or a high-level language such as python, R, julia, or matlab. The language used in the exercises and the final exam will be R (https://www.r-project.org) exclusively. If you don't know it already, some extra effort will be required for the exercises.
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer
Abstract5 to 6 talks on applied statistics.
ObjectiveKennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
ContentIn 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 notesBei 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 / NoticeDies 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.
401-6228-00LProgramming with R for Reproducible Research Information 1 credit1GM. Mächler
AbstractDeeper 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.
Objective
ContentSee https://stat.ethz.ch/education/semesters/ss2014/Progr_R3
Lecture notesMaterial available from
https://stat.ethz.ch/education/semesters/ss2014/Progr_R3
LiteratureNorman 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 / NoticeR 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