The spring semester 2021 will take place online until further notice. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.

Nicolai Meinshausen: Catalogue data in Spring Semester 2016

Name Prof. Dr. Nicolai Meinshausen
FieldStatistics
Address
Professur für Statistik
ETH Zürich, HG G 24.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 32 74
E-mailmeinshausen@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~nicolai
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
401-3620-16LSeminar in Statistics: Learning Blackjack Restricted registration - show details
Number of participants limited to 18.

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 credits2SJ. Peters, P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer
AbstractIn this seminar, we study different methods that can be applied to the problem of finding a good strategy to play Blackjack. Since the machine does not know the rules of Blackjack, it adopts (and modifies) random strategies. The data for learning will be the games that have been played. Some parts of the seminar will be devoted to implementing these methods in python.
ObjectiveAfter this seminar, you should know
- the problem of reinforcement learning,
- inverse probability weighting and its relation to causality,
- Q-learning,
- contextual multi-armed bandits and
- the optimal strategy of playing BlackJack.
Prerequisites / NoticeWe require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics and basic knowledge in computer programming.

Topics will be assigned during the first meeting.
401-3622-00LRegression8 credits4GN. Meinshausen
AbstractIn regression, the dependency of a random response variable on other variables is examined. We consider the theory of linear regression with one or more covariates, nonlinear models and generalized linear models, robust methods, model choice and nonparametric models. Several numerical examples will illustrate the theory.
ObjectiveEinführung in Theorie und Praxis eines umfassenden und vielbenutzten Teilgebiets der angewandten Statistik, unter Berücksichtigung neuerer Entwicklungen.
ContentIn der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression, Numerik, Ridge Regression. Durchrechnung und Diskussion von Anwendungsbeispielen.
Lecture notesLecture notes
Prerequisites / NoticeCredits cannot be recognised for both courses 401-3622-00L Regression and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden).
401-4620-00LStatistics Lab Restricted registration - show details
Number of participants limited to 27.
6 credits2SM. Kalisch, M. H. Maathuis, L. Meier, N. Meinshausen
Abstract"Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It
provides a learning environment in a realistic setting.

Students lead a regular consulting session at the Seminar für Statistik
(SfS). After the session, the statistical data analysis is carried out and
a written report and results are presented to the client. The project is
also presented in the course's seminar.
Objective- gain initial experience in the consultancy process
- carry out a consultancy session and produce a report
- apply theoretical knowledge to an applied problem

After the course, students will have practical knowledge about statistical
consulting. They will have determined the scientific problem and its
context, enquired the design of the experiment or data collection, and
selected the appropriate methods to tackle the problem. They will have
deepened their statistical knowledge, and applied their theoretical
knowledge to the problem. They will have gathered experience in explaining
the relevant mathematical and software issues to a client. They will have
performed a statistical analysis using R (or SPSS). They improve their
skills in writing a report and presenting statistical issues in a talk.
ContentStudents participate in consulting meetings at the SfS. Several consulting
dates are available for student participation. These are arranged
individually.

-During the first meeting the student mainly observes and participates in
the discussion. During the second meeting (with a different client), the
student leads the meeting. The member of the consulting team is overseeing
(and contributing to) the meeting.

-After the meeting, the student performs the recommended analysis, produces
a report and presents the results to the client.

-Finally, the student presents the case in the weekly course seminar in a
talk. All students are required to attend the seminar regularly.
Lecture notesn/a
LiteratureThe required literature will depend on the specific statistical problem
under investigation. Some introductory material can be found below.
Prerequisites / NoticePrerequisites:
Sound knowledge in basic statistical methods, especially regression and, if
possible, analysis of variance. Basic experience in Data Analysis with R
and/or SPSS.

Useful background lectures and material:
-Applied Statistical Regression (Dr. Marcel Dettling)
http://stat.ethz.ch/education/semesters/as2010/semesters/as2010/asr
-Angewandte statistische Regression, mit Ergänzung
(Prof. Werner Stahel, Dr. Markus Kalisch)
Script: http://stat.ethz.ch/~stahel/courses/regression/
-Applied Analysis of Variance and Experimental Design (Prof. M Müller) http://stat.ethz.ch/education/semesters/as2010/anova
-W. Stahel, Statistische Datenanalyse: Eine Einführung für
Naturwissenschaftler, (5. Auflage), Vieweg, 2005.

Useful material on Statistical Software (R and/or SPSS):
-401-6215-00L Using R for Statistical Data Analysis and Graphics (Dr. M. Mächler, Dr. A. J. Papritz, Dr. C. B. Schwierz). An older version of this course can be found on: http://stat.ethz.ch/ stahel/courses/R/
-An Introduction to R. http://stat.ethz.ch/CRAN/doc/manuals/R-intro.pdf
-SPSS Course and Exercises: ftp://stat.ethz.ch/U/sfs/SPSSKurs/
-Andy Field, Discovering Statistics Using SPSS, 3rd Edition, 2009, SAGE.
401-5620-00LResearch Seminar on Statistics Information 0 credits2KP. L. Bühlmann, L. Held, T. Hothorn, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf
AbstractResearch colloquium
Objective
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.