Thomas Hofmann: Katalogdaten im Frühjahrssemester 2021 |
| Name | Herr Prof. Dr. Thomas Hofmann |
| Lehrgebiet | Datenanalytik |
| Adresse | Dep. Informatik ETH Zürich, CAB F 48.1 Universitätstrasse 6 8092 Zürich SWITZERLAND |
| thomas.hofmann@inf.ethz.ch | |
| URL | http://www.inf.ethz.ch/department/faculty-profs/person-detail.html?persid=148752 |
| Departement | Informatik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 252-0945-12L | Doctoral Seminar Machine Learning (FS21) 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 KP | 1S | N. He, M. Sachan, J. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch | |
| Kurzbeschreibung | 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. | ||||
| Lernziel | 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. | ||||
| Voraussetzungen / Besonderes | 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. | ||||
| 263-0008-00L | Computational Intelligence Lab Only for master students, otherwise a special permission by the study administration of D-INFK is required. | 8 KP | 2V + 2U + 3A | T. Hofmann | |
| Kurzbeschreibung | This laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications. | ||||
| Lernziel | Students acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and experimental validation. This lab course has a strong focus on practical assignments. Students work in groups of three to four people, to develop solutions to three application problems: 1. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery. For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper. (Disclaimer: The offered projects may be subject to change from year to year.) | ||||
| Inhalt | see course description | ||||
| 401-5680-00L | Foundations of Data Science Seminar  | 0 KP | P. L. Bühlmann, A. Bandeira, 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, F. Yang | ||
| Kurzbeschreibung | Research colloquium | ||||
| Lernziel | |||||