Universität Wien

053621 VU Mining Massive Data (2025S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle
Mo 05.05. 09:45-11:15 Seminarraum 7, Währinger Straße 29 1.OG

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

  • Montag 03.03. 09:45 - 11:15 Digital
  • Donnerstag 06.03. 15:00 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Montag 10.03. 09:45 - 11:15 Digital
  • Donnerstag 13.03. 15:00 - 16:30 Digital
  • Montag 17.03. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 20.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Montag 24.03. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 27.03. 15:00 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Montag 31.03. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 03.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Montag 07.04. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 10.04. 15:00 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Montag 28.04. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 08.05. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Montag 12.05. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 15.05. 15:00 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Montag 19.05. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 22.05. 15:00 - 16:30 Hörsaal II NIG Erdgeschoß
  • Montag 26.05. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Montag 02.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 05.06. 15:00 - 16:30 Hörsaal II NIG Erdgeschoß
  • Donnerstag 12.06. 15:00 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Montag 16.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Montag 23.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Donnerstag 26.06. 15:00 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Montag 30.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Goals:
Upon successful participation in the course, students will understand the principles of state-of-the-art techniques for learning from massive data. They can apply and evaluate those techniques in practical applications.

Lecture Contents:
* Dealing with large data (e.g., Map-Reduce)
* Fast nearest neighbor methods (e.g., Locality Sensitive Hashing)
* Scalable Supervised Learning, Online learning
* Active learning
* Clustering
* Interactive learning

Methods:
Lecture
+ pen & paper exercises (~bi-weekly assignments) and their discussion
+ programming exercises

Art der Leistungskontrolle und erlaubte Hilfsmittel

Written exam (at the end of the semester; 2xA4 sheets of hand-written notes can be used)

3 Programming assignments (submission of solutions in the form of source code and a written report)

5 Pen & paper exercises (some containing small programming tasks; ~bi-weekly; solved by students before the exercise sessions in which the students are randomly selected to present their solutions; discussion; attendance is mandatory)

Mindestanforderungen und Beurteilungsmaßstab

Students attending this course must have solid basic knowledge of statistics, algorithms, machine learning, and programming (i.e., appropriate university-level courses must have been taken and all prerequisites according to the curriculum must be met).

40% Written exam
35% Programming exercises
25% Pen & paper exercises (with small programming tasks)

P = Average percentage on the written exam, the programming exercises, and the pen & paper exercises

90% <= P Sehr Gut (1)
77% <= P < 90% Gut (2)
62% <= P < 77% Befriedigend (3)
50% <= P < 62% Genügend (4)
0% <= P < 50% Nicht Genügend (5)

At least 50% on the written exam, 50% on the programming exercises, and 50% on the pen & paper exercises must be achieved for a passing grade.

Attendance of the lecture parts of the course is voluntary but highly recommended. Attendance of the pen & paper exercises and the written exam is compulsory to pass the course.

Prüfungsstoff

The presented topics in the lecture (according to slides + exercises). Referenced Literature (as indicated in detail on lecture slides).

Literatur

Ester M., Sander J. Knowledge Discovery in Databases: Techniken und Anwendungen.
J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets.
J. Han, M. Kamber, J.Pei.Data Mining: Concepts and Techniques.
I. H. Witten , E. Frank, M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques.
+ papers mentioned lecture slides

Zuordnung im Vorlesungsverzeichnis

Modul: MMD

Letzte Änderung: Mi 19.03.2025 13:45