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

SemesterSpring Semester 2020
LecturersF. Perez Cruz
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber 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.


263-5225-00 SAdvanced Topics in Machine Learning and Data Science2 hrs
Wed16:15-18:00LFW E 13 »
F. Perez Cruz

Catalogue data

AbstractIn 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.
ObjectiveThe 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.
ContentThe 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.
LiteratureThe papers will be presented in the first session of the seminar.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersF. Perez Cruz
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


Places20 at the most
PriorityRegistration for the course unit is only possible for the primary target group
Primary target groupData Science MSc (261000)
Computer Science MSc (263000)
CAS ETH in Computer Science (269000)
Computer Science (Mobility) (274000)
Waiting listuntil 01.03.2020

Offered in

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Data Science MasterSeminarWInformation
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Computer Science MasterSeminar in Information SystemsWInformation