Universität Wien
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052321 VU Recent Developments in Knowledge Discovery in Databases (2024S)

6.00 ECTS (4.00 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: Deutsch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Außerhalb der Ferienzeiten
- Mittwoch 13:15-14:45, PC 3 in der Kolingasse und
- Donnerstag 13:15-14:45, SR 5 in der Währingerstraße

  • Mittwoch 06.03. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 07.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 13.03. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 14.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 20.03. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 21.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 10.04. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 11.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 17.04. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 18.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 24.04. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 25.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Donnerstag 02.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 08.05. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Mittwoch 15.05. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 16.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 22.05. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 23.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 29.05. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Mittwoch 05.06. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 06.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 12.06. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 13.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 19.06. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 20.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Mittwoch 26.06. 13:15 - 14:45 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 27.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The amount of data gathered every year is steadily increasing, and mankind is curious for more and more knowledge that they can discover in these vast amounts of data. Even though data is a mighty resource, we are not using its full potential:
Surprisingly much (if not most) knowledge that is discovered in data nowadays is found with methods developed already in the 20th century.
Most data scientists do not stay updated with new developments and, thus, results in research and industry do not reach the high quality of state-of-the-art methods.
This course aims at teaching recently developed, important state-of-the-art methods to discover knowledge in databases– from a theoretical point of view as well as their implementation and application. We learn how to discover, assess, and deeply understand novel methods that are more complex than fundamental methods taught in other courses. We address different aspects of learning new methods from the field of knowledge discovery in databases: learning lecture-style by listening to talks, discovering a new method by reading a scientific paper, implementing it, teaching it to others in a talk as well as discussing it in groups and writing a report as a team.

This semester, we focus on clustering methods and causality.

Methods/ Course:
The first part of the course will consist of live lectures teaching the basics.
We start with interactive lectures about clustering. During that, students will start their individual projects by reading about one recently published method from the field of clustering in detail.
After that, there will be lectures on causality with a small test at the end.

The second part is more hands-on: students will have time to implement their chosen method in python and prepare a talk about it.
Students will give talks in groups covering similar topics and write a paper in team work about their findings where they compare their methods experimentally within the group as well as theoretically with both, their group and the others.

Art der Leistungskontrolle und erlaubte Hilfsmittel

- A small test end of April/ beginning of May about clustering and causality
- A programming exercise including peer review (individually)
- A written report about recently developed methods from the field clustering (in teamwork)
- A talk complementing the report (teamwork, graded individually)

Mindestanforderungen und Beurteilungsmaßstab

This course is for master students only.
We recommend to have visited the basic bachelor courses as well as
- Data Mining
- Foundations of Data Analysis

To pass the course, you need to pass all four constituents – test, programming exercise, talk, and report – as described above.
In order to successfully pass the course, regular attendance is strongly recommended.
All assignments need to be done alone or with the assigned group, no external aiding people are allowed. It is not allowed to use any ChatBots or similar (e.g., ChatGPT) for writing the report.

Prüfungsstoff

Literatur

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 31.07.2024 11:25