Peter L. Bühlmann: Catalogue data in Spring Semester 2019 |
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 |
peter.buehlmann@stat.math.ethz.ch | |
URL | http://stat.ethz.ch/~peterbu |
Department | Mathematics |
Relationship | Full Professor |
Number | Title | ECTS | Hours | Lecturers | |
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401-3620-19L | Student Seminar in Statistics: Adversarial and Robust Machine Learning Number of participants limited to 22. 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 credits | 2S | P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer | |
Abstract | As 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. | ||||
Objective | After 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 | ||||
Content | As 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. | ||||
Prerequisites / Notice | We 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. | ||||
401-5000-00L | Zurich Colloquium in Mathematics | 0 credits | S. Mishra, P. L. Bühlmann, A. Iozzi, R. Pandharipande, University lecturers | ||
Abstract | The lectures try to give an overview of "what is going on" in important areas of contemporary mathematics, to a wider non-specialised audience of mathematicians. | ||||
Objective | |||||
401-5620-00L | Research Seminar on Statistics | 0 credits | 2K | P. L. Bühlmann, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf | |
Abstract | Research colloquium | ||||
Objective | |||||
401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. 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 | |
Abstract | 5 to 6 talks on applied statistics. | ||||
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. | ||||
401-5680-00L | Foundations of Data Science Seminar | 0 credits | P. 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 | ||
Abstract | Research colloquium | ||||
Objective |