Andreas Krause: Katalogdaten im Herbstsemester 2020

Auszeichnung: Die Goldene Eule
NameHerr Prof. Dr. Andreas Krause
LehrgebietInformatik
Adresse
Institut für Maschinelles Lernen
ETH Zürich, OAT Y 13.1
Andreasstrasse 5
8092 Zürich
SWITZERLAND
Telefon+41 44 632 63 22
Fax+41 44 623 15 62
E-Mailkrausea@ethz.ch
URLhttp://las.ethz.ch/krausea.html
DepartementInformatik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
252-0945-11LDoctoral Seminar Machine Learning (HS20) Belegung eingeschränkt - Details anzeigen
Only for Computer Science Ph.D. students.

This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar.
2 KP1SJ. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch
KurzbeschreibungAn essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
LernzielThe seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills.
Voraussetzungen / BesonderesThis doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab.
263-5210-00LProbabilistic Artificial Intelligence Information Belegung eingeschränkt - Details anzeigen 8 KP3V + 2U + 2AA. Krause
KurzbeschreibungThis course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics and the Internet.
LernzielHow can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for graduate students.
InhaltTopics covered:
- Probability
- Probabilistic inference (variational inference, MCMC)
- Bayesian learning (Gaussian processes, Bayesian deep learning)
- Probabilistic planning (MDPs, POMPDPs)
- Multi-armed bandits and Bayesian optimization
- Reinforcement learning
Voraussetzungen / BesonderesSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
401-5680-00LFoundations of Data Science Seminar Information 0 KPP. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, G. Rätsch, C. Uhler, S. van de Geer, F. Yang
KurzbeschreibungResearch colloquium
Lernziel