Universität Wien
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053621 VU Mining Massive Data (2024S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 01.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
    PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Monday 04.03. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 08.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
    PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Monday 11.03. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 15.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
    PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Monday 18.03. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 22.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 08.04. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 12.04. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 15.04. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 19.04. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 22.04. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 26.04. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 29.04. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 03.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 06.05. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 10.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 13.05. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 17.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 24.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 27.05. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 31.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 03.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 07.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 10.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 14.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 17.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 21.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 24.06. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 28.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG

Information

Aims, contents and method of the course

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

Assessment and permitted materials

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)

6 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)

Minimum requirements and assessment criteria

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).

30% Written exam
35% Programming exercises
35% 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.

Examination topics

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

Reading list

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

Association in the course directory

Modul: MMD

Last modified: Fr 15.03.2024 13:05