Das Frühjahrssemester 2021 findet sicher bis Ostern online statt. Ausnahmen: Veranstaltungen, die nur mit Präsenz vor Ort durchführbar sind. Bitte beachten Sie die Informationen der Dozierenden.

Marloes H. Maathuis: Katalogdaten im Frühjahrssemester 2019

NameFrau Prof. Dr. Marloes H. Maathuis
LehrgebietStatistik
Adresse
Seminar für Statistik (SfS)
ETH Zürich, HG G 15.1
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 61 84
E-Mailmarloes.maathuis@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~maathuis
DepartementMathematik
BeziehungOrdentliche Professorin

NummerTitelECTSUmfangDozierende
401-3620-19LStudent Seminar in Statistics: Adversarial and Robust Machine Learning Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 22

Hauptsächlich für Studierende der Bachelor- und Master-Studiengänge Mathematik, welche nach der einführenden Lerneinheit 401-2604-00L Wahrscheinlichkeit und Statistik (Probability and Statistics) mindestens ein Kernfach oder Wahlfach in Statistik besucht haben. Das Seminar wird auch für Studierende der Master-Studiengänge Statistik bzw. Data Science angeboten.
4 KP2SP. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer
KurzbeschreibungAs statistical and machine learning models are increasingly employed in many real-world applications it becomes more important to understand the vulnerabilities and robustness properties of these models.
In the first part of this seminar, we will study papers relating to adversarial examples. In the second part of the course, we will review other types of distribution shifts.
LernzielAfter this seminar, you should know
- properties of adversarial examples
- some attacks and defenses
- some concepts from robust optimization and distributional robustness
- other distribution shifts that can fool machine learning models in general and neural networks in particular
InhaltAs statistical and machine learning models are increasingly employed in many real-world applications it becomes more important to understand the vulnerabilities and robustness properties of these models. In the first part of this seminar, we will study papers relating to adversarial examples, covering their properties, various attacks and defenses. In the second part of the course, we will review other types of distribution shifts, posing significant challenges for state-of-the-art machine learning models. Some parts of the seminar will be devoted to implementing these methods in python.
Voraussetzungen / BesonderesWe require at least one course in statistics or machine learning and basic knowledge in computer programming. Some background knowledge in deep learning is helpful but not strictly required.
Topics will be assigned during the first meeting.
401-3632-00LComputational Statistics8 KP3V + 1UM. H. Maathuis
KurzbeschreibungWe discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.
LernzielThe student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R.
Voraussetzungen / BesonderesAt least one semester of (basic) probability and statistics.

Programming experience is helpful but not required.
401-4620-00LStatistics Lab Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 27
6 KP2SM. Kalisch, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen
Kurzbeschreibung"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.
Lernziel- 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.
InhaltStudents 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.
Skriptn/a
LiteraturThe required literature will depend on the specific statistical problem
under investigation. Some introductory material can be found below.
Voraussetzungen / BesonderesPrerequisites:
Sound knowledge in basic statistical methods, especially regression and, if
possible, analysis of variance. Basic experience in Data Analysis with R.
401-5620-00LResearch Seminar on Statistics Information 0 KP2KP. L. Bühlmann, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf
KurzbeschreibungForschungskolloquium
Lernziel
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 KP1KM. 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
Kurzbeschreibung5 bis 6 Vorträge zur angewandten Statistik.
LernzielKennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
InhaltIn 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.
SkriptBei 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.
Voraussetzungen / BesonderesDies 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.
401-5680-00LFoundations of Data Science Seminar Information 0 KPP. L. Bühlmann, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, N. Meinshausen, G. Rätsch, S. van de Geer
KurzbeschreibungResearch colloquium
Lernziel