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Thomas Hofmann: Katalogdaten im Herbstsemester 2017

NameHerr Prof. Dr. Thomas Hofmann
LehrgebietDatenanalytik
Adresse
Dep. Informatik
ETH Zürich, CAB F 48.1
Universitätstrasse 6
8092 Zürich
SWITZERLAND
E-Mailthomas.hofmann@inf.ethz.ch
URLhttp://www.inf.ethz.ch/department/faculty-profs/person-detail.html?persid=148752
DepartementInformatik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
252-0341-01LInformation Retrieval Information
Findet dieses Semester nicht statt.
4 KP2V + 1UT. Hofmann
KurzbeschreibungIntroduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
LernzielIn depth understanding of managing, indexing, and retrieving documents with text, image and XML content. Knowledge about basic search algorithms on the web, benchmarking of search algorithms, and relevance feedback methods.
252-0945-05LDoctoral Seminar Machine Learning (HS17) Belegung eingeschränkt - Details anzeigen
Nur für Doktoranden vom D-INFK.
1 KP2SJ. 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.
252-5051-00LAdvanced Topics in Machine Learning Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 40.
2 KP2SJ. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch
KurzbeschreibungIn 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.
LernzielThe 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.
InhaltThe 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.
LiteraturThe papers will be presented in the first session of the seminar.
263-3210-00LDeep Learning Information Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 300
4 KP2V + 1UT. Hofmann
KurzbeschreibungDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
LernzielIn recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Voraussetzungen / BesonderesThis is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.

The participation in the course is subject to the following conditions:
1) The number of participants is limited to 300 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Machine Learning
https://ml2.inf.ethz.ch/courses/ml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2017/CIL/

Learning and Intelligent Systems
https://las.inf.ethz.ch/teaching/lis-s17

Statistical Learning Theory
http://ml2.inf.ethz.ch/courses/slt/

Computational Statistics
https://stat.ethz.ch/education/semesters/ss2012/CompStat/sk.pdf

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f16

Data Mining: Learning from Large Data Sets
https://las.inf.ethz.ch/teaching/dm-f16
851-0147-03LBedeutung und Information
Besonders geeignet für Studierende D-INFK
3 KP2SM. Hampe, T. Hofmann
KurzbeschreibungIm Seminar werden vergleichend Theorien der Bedeutung und Information anhand exemplarischer Texte u.a. von Paul Grice und Fred Dretske studiert.
LernzielDie Studierenden sollen die unterschiedlichen philosophischen Ansätze zur Explikation des "Gehaltes" sprachlicher Mitteilungen kennen lernen und vor allem sich über die Differenz zwischen intentionalen und nicht-intentionalen Konzepten ein Urteil bilden können.