Andreas Krause: Catalogue data in Autumn Semester 2016

Name	Prof. Dr. Andreas Krause
Field	Computer Science
Address	Institut für Maschinelles Lernen ETH Zürich, OAT Y 13.1 Andreasstrasse 5 8092 Zürich SWITZERLAND
Telephone	+41 44 632 63 22
Fax	+41 44 623 15 62
E-mail	krausea@ethz.ch
URL	http://las.ethz.ch/krausea.html
Department	Computer Science
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
252-0945-03L	Doctoral Seminar Machine Learning (HS16) Only for Computer Science Ph.D. students.	2 credits	2S	J. M. Buhmann, T. Hofmann, A. Krause
Abstract	An 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.
Learning objective	The 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.
Prerequisites / Notice	This 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.
252-5051-00L	Advanced Topics in Machine Learning	2 credits	2S	J. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch
Abstract	In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.
Learning objective	The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.
Content	The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models.
Literature	The papers will be presented in the first session of the seminar.
263-5200-00L	Data Mining: Learning from Large Data Sets	4 credits	2V + 1U	A. Krause
Abstract	Many scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications.
Learning objective	Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, we will study principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course will both cover theoretical foundations and practical applications.
Content	Topics covered: - Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk) - Fast nearest neighbor methods (Shingling, locality sensitive hashing) - Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines) - Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback) - Active learning (uncertainty sampling, pool-based methods, label complexity) - Dimension reduction (random projections, nonlinear methods) - Data streams (Sketches, coresets, applications to online clustering) - Recommender systems
Prerequisites / Notice	Prerequisites: Solid basic knowledge in statistics, algorithms and programming. Background in machine learning is helpful but not required.