Gunnar Rätsch: Katalogdaten im Herbstsemester 2022 |
Name | Herr Prof. Dr. Gunnar Rätsch |
Lehrgebiet | Biomedizininformatik |
Adresse | Professur für Biomedizininformatik ETH Zürich, CAB F 53.2 Universitätstrasse 6 8092 Zürich SWITZERLAND |
Telefon | +41 44 632 20 36 |
raetsch@inf.ethz.ch | |
URL | http://bmi.inf.ethz.ch |
Departement | Informatik |
Beziehung | Ordentlicher Professor |
Nummer | Titel | ECTS | Umfang | Dozierende | |
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252-0945-15L | Doctoral Seminar Machine Learning (HS22) 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, T. Hofmann, A. Krause, G. Rätsch, M. Sachan | |
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. | ||||
263-5100-00L | Topics in Medical Machine Learning Number of participants limited to 18. The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | 2 KP | 2S | G. Rätsch, J. Vogt | |
Kurzbeschreibung | This seminar discusses recent relevant contributions to the fields of medical machine learning and related areas. Each participant will hold a presentation and lead the subsequent discussion. | ||||
Lernziel | Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to successfully present the key points of existing research work are the same as those needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class. | ||||
Inhalt | The topics covered in the seminar are related to recent computational challenges that arise in the medical field, including but not limited to clinical data analysis, interpretable machine learning, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are on the list of selected papers. | ||||
Voraussetzungen / Besonderes | Knowledge of machine learning and interest in applications in medicine. ML4H is beneficial as a prior course. |