Peter L. Bühlmann: Catalogue data in Spring Semester 2015

Name	Prof. Dr. Peter L. Bühlmann
Field	Mathematik
Address	Seminar für Statistik (SfS) ETH Zürich, HG G 17 Rämistrasse 101 8092 Zürich SWITZERLAND
Telephone	+41 44 632 73 38
Fax	+41 44 632 12 28
E-mail	peter.buehlmann@stat.math.ethz.ch
URL	http://stat.ethz.ch/~peterbu
Department	Mathematics
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
401-3620-15L	Seminar in Statistics: High-Dimensional Statistics Number of participants limited to 24. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics	4 credits	2S	N. Meinshausen, P. L. Bühlmann, M. H. Maathuis, S. van de Geer
Abstract
Learning objective	The seminar familiarizes students with the basic techniques of high-dimensional data analysis. Both theoretical concepts and practical implementation of methods will be discussed. Students will learn how to study a given topic from a book or a research paper in groups of two and how to prepare an oral presentation which is understandable to other students in the seminar. To achieve this goal, students meet twice. one and two weeks before their presentation, with an assistant or one of the lecturers.
Prerequisites / Notice	We require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics. Topics will be assigned during the first meeting.
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.
401-5000-00L	Zurich Colloquium in Mathematics	0 credits		P. L. Bühlmann, T. Kappeler, A. Kresch, S. Mishra, R. Pandharipande, V. Schroeder
Abstract
Learning objective
401-5620-00L	Research Seminar on Statistics	0 credits	2K	P. L. Bühlmann, L. Held, T. Hothorn, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf
Abstract	Research colloquium
Learning objective
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.