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.
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.
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Repetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum Prüfungsmodus
Students will be assessed based on their seminar presentation (70%) and contribution to discussions of all presentations (30%). Attendance in all but one seminar week is required.
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.
Gruppen
Keine Informationen zu Gruppen vorhanden.
Einschränkungen
Plätze
Maximal 18
Vorrang
Die Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich
Primäre Zielgruppe
Data Science MSc (261000)
Informatik MSc (263000)
CAS ETH in Informatik (269000)
Informatik (Mobilität) (274000)
Leistungskontrolle als Semesterkurs