Universität Wien
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053630 SE Research Seminar (2024W)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Donnerstag 03.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02 (Vorbesprechung)
  • Donnerstag 24.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 31.10. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 07.11. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 14.11. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 21.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 28.11. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 05.12. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 12.12. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 09.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 16.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 23.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 30.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This seminar aims to provide an overview of the current state-of-the-art data science research, highlighting key techniques and methods used in the field. The seminar will be divided into two segments. In the first part, attendees will learn about the data science research process by reading and critically evaluating scientific papers. The second part is dedicated to a practical approach where participants get hands-on experience with the tools
and methods used in applied data science research to get a sense of the process involved in applying these tools to real-world problems.

Goals of Part 1
By presenting a published research work, participants learn about the latest developments and challenges in fields such as machine learning, natural language processing, computer vision, mathematical aspects of deep learning, applied data science, and other related areas. In particular, students learn how to present and identify a research question, communicate results, and critically evaluate the quality and relevance of a paper.

Goals of Part 2
The research seminar’s coding portion is divided into four programming exercises posted and graded throughout the semester. The students are randomly selected to present their exercises in class, where their work is evaluated.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Part I: Paper presentations
Paper pitch presentation (15%)
Final paper presentation (35%)

Part II: Coding exercises
Accounts for a total of 50% of the final grade (further subdivided by individual assignments). This portion of the grade depends on the total number of completed exercises alongside a successful presentation of one's work in front of the class.

Mindestanforderungen und Beurteilungsmaßstab

Half the achievable points are mandatory for an overall passing grade. Additionally, you are allowed to have up to two unexcused absences.

The grading scale for the course will be:
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%

Prüfungsstoff

See above & the reading list below.

Literatur

A list of topics and the respective scientific papers will be made available via Moodle and discussed during the seminar's first session on 03.10.2024.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 11.09.2024 11:25