263-5225-00L  Advanced Topics in Machine Learning and Data Science

SemesterFrühjahrssemester 2021
DozierendeF. Perez Cruz
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarNumber of participants limited to 20.

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.



Lehrveranstaltungen

NummerTitelUmfangDozierende
263-5225-00 SAdvanced Topics in Machine Learning and Data Science2 Std.
Mi16:15-18:00LFW E 13 »
F. Perez Cruz

Katalogdaten

KurzbeschreibungIn this seminar, recent papers of the machine learning and data science literature are presented and discussed. Possible topics cover statistical models, machine learning algorithms and its applications.
LernzielThe seminar “Advanced Topics in Machine Learning and Data Science” familiarizes students with recent developments in machine learning and data science. Recently published articles, as well as influential papers, have to be presented and critically reviewed. The students will learn how to structure a scientific presentation, which covers the motivation, key ideas and main results of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth for the audience to be able to follow its main conclusion, especially why the article is (or is not) worth attention. 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 machine learning and data science literatures. The topics will vary from year to year but they are centered on methodological issues in machine learning and its application, not only to text or images, but other scientific
domains like medicine, climate or physics.
LiteraturThe papers will be presented in the first session of the seminar.

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte2 KP
PrüfendeF. Perez Cruz
Formbenotete Semesterleistung
PrüfungsspracheEnglisch
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.

Lernmaterialien

 
HauptlinkInformation
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

PlätzeMaximal 20
VorrangDie Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich
Primäre ZielgruppeData Science MSc (261000)
Informatik MSc (263000)
CAS ETH in Informatik (269000)
Informatik (Mobilität) (274000)
WartelisteBis 07.03.2021

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