Universität Wien

053621 VU Mining Massive Data (2021S)

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

  • Monday 01.03. 09:45 - 11:15 Digital
  • Friday 05.03. 09:45 - 11:15 Digital
  • Monday 08.03. 09:45 - 11:15 Digital
  • Monday 15.03. 09:45 - 11:15 Digital
  • Friday 19.03. 09:45 - 11:15 Digital
  • Monday 22.03. 09:45 - 11:15 Digital
  • Friday 26.03. 09:45 - 11:15 Digital
  • Monday 12.04. 09:45 - 11:15 Digital
  • Friday 16.04. 09:45 - 11:15 Digital
  • Monday 19.04. 09:45 - 11:15 Digital
  • Friday 23.04. 09:45 - 11:15 Digital
  • Monday 26.04. 09:45 - 11:15 Digital
  • Friday 30.04. 09:45 - 11:15 Digital
  • Monday 03.05. 09:45 - 11:15 Digital
  • Friday 07.05. 09:45 - 11:15 Digital
  • Monday 10.05. 09:45 - 11:15 Digital
  • Friday 14.05. 09:45 - 11:15 Digital
  • Monday 17.05. 09:45 - 11:15 Digital
  • Friday 21.05. 09:45 - 11:15 Digital
  • Friday 28.05. 09:45 - 11:15 Digital
  • Monday 31.05. 09:45 - 11:15 Digital
  • Friday 04.06. 09:45 - 11:15 Digital
  • Monday 07.06. 09:45 - 11:15 Digital
  • Friday 11.06. 09:45 - 11:15 Digital
  • Monday 14.06. 09:45 - 11:15 Digital
  • Friday 18.06. 09:45 - 11:15 Digital
  • Monday 21.06. 09:45 - 11:15 Digital
  • Friday 25.06. 09:45 - 11:15 Digital
  • Monday 28.06. 09:45 - 11:15 Digital

Information

Aims, contents and method of the course

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

Lecture Contents:
* Dealing with large data (e.g., Map-Reduce, Spark)
* Fast nearest neighbor methods (e.g., Locality Senistive Hashing)
* Scalable Supervised Learning, Online learning
* Active learning
* Dimension reduction
* Clustering
* Bandits
* Recommender systems

Method:
Lecture (recorded lectures will be made available via Moodle) + pen & paper exercises and their discussion
+ programming exercises

Assessment and permitted materials

Written exam
Programming exercises
Pen & paper exercises and their (live) discussion

Minimum requirements and assessment criteria

It is recommended that students attending this course have solid basic knowledge in statistics, algorithms and programming.

30% Written exam
40% Programming exercises
30% Pen & paper exercises (with minor programming exercises)

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

85% <= P <= % Sehr Gut (1)
74% <= P < 85% Gut (2)
62% <= P < 74% 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.

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 12.05.2023 00:13