Sara van de Geer: Catalogue data in Autumn Semester 2018

Name Prof. em. Dr. Sara van de Geer
Seminar für Statistik (SfS)
ETH Zürich, HG GO 14.2
Rämistrasse 101
8092 Zürich
Telephone+41 44 632 22 52
RelationshipProfessor emerita

401-3620-68LStudent Seminar in Statistics: Statistical Learning with Sparsity Restricted registration - show details
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. Also offered in the Master Programmes Statistics resp. Data Science.
4 credits2SM. Mächler, M. H. Maathuis, N. Meinshausen, S. van de Geer
AbstractWe study selected chapters from the 2015 book "Statistical Learning with Sparsity" by Trevor Hastie, Rob Tibshirani and Martin Wainwright.

(details see below)
ObjectiveDuring this seminar, we will study roughly one chapter per week from the book. You will obtain a good overview of the field of sparse & high-dimensional modeling of modern statistics.
Moreover, you will practice your self-studying and presentation skills.
Content(From the book's preface:) "... summarize the actively developing
field of statistical learning with sparsity.
A sparse statistical model is one having only a small number of nonzero parameters or weights. It represents a classic case of “less is more”: a sparse model can be much easier to estimate and interpret than a dense model.
In this age of big data, the number of features measured on a person or object can be large, and might be larger than the number of observations. The sparsity assumption allows us to tackle such problems and extract useful and reproducible patterns from big datasets."

For presentation of the material, occasionally you'd consider additional published research, possibly e.g., for "High-Dimensional Inference"
Lecture notesWebsite: with groups, FAQ, topics, slides, and Rscripts :
LiteratureTrevor Hastie, Robert Tibshirani, Martin Wainwright (2015)
Statistical Learning with Sparsity: The Lasso and Generalization
Monographs on Statistics and Applied Probability 143
Chapman Hall/CRC
ISBN 9781498712170

Access :

(full access via ETH (library) network, if inside ETH (VPN))

- Author's website (includes errata, updated pdf, data):
Prerequisites / NoticeWe require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics, as well as some experience with the statistical software R.

Topics will be assigned during the first meeting.
401-3621-00LFundamentals of Mathematical Statistics Information 10 credits4V + 1US. van de Geer
AbstractThe course covers the basics of inferential statistics.
401-5620-00LResearch Seminar on Statistics Information 0 credits2KL. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf
AbstractResearch colloquium
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer
AbstractAbout 5 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 5 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
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.