Sara van de Geer: Catalogue data in Spring Semester 2016

Name Prof. em. Dr. Sara van de Geer
FieldMathematic
Address
Seminar für Statistik (SfS)
ETH Zürich, HG GO 14.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 22 52
E-mailsara.vandegeer@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~vsara
DepartmentMathematics
RelationshipProfessor emerita

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.
401-4628-16LEstimation and Testing under Sparsity4 credits2VS. van de Geer
AbstractIn high-dimensional models the number of parameters p is larger than the number of observations n. Therefore, classical (asymptotic) theory needs new methods and paradigms for estimation and testing. One of the key concepts here is "sparsity" which says that most of the parameters are actually not relevant and can be set to zero.
Objective
ContentIn high-dimensional models the number of parameters p is larger than the number of observations n. Therefore, classical (asymptotic) theory needs new methods and paradigms for estimation and testing. One of the key concepts here is "sparsity" which says that most of the parameters are actually not relevant and can be set to zero. A popular way to take sparsity into account is regularizing using the l_1-penalty. This leads to two lines of research. Firstly, we need to study the statistical properties of l_1-regularized estimators and related issues, for example their role as initial estimators in a one-step procedure for the construction of asymptotically linear estimators. Secondly, the l_1-approach has a special geometry which one can study in terms of properties of empirical processes. Therefore the lectures have two intertwined parts: one where statistical theory plays the main role and a second where probability theory is studied. Most results presented will be given a full proof, perhaps with parts left as exercises.
LiteratureThe course will be based on lecture notes to appear (Springer)
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.
406-3621-AALFundamentals of Mathematical Statistics
Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
10 credits21RS. van de Geer
AbstractThe course covers the basics of inferential statistics.
Objective