Universität Wien

053621 VU Mining Massive Data (2022S)

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 04.03. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 07.03. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Monday 14.03. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 18.03. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Seminarraum 6, Währinger Straße 29 1.OG
Monday 21.03. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 25.03. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 28.03. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 01.04. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Seminarraum 6, Währinger Straße 29 1.OG
Monday 04.04. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 08.04. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 25.04. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 29.04. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Seminarraum 6, Währinger Straße 29 1.OG
Monday 02.05. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 06.05. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 09.05. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 13.05. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Seminarraum 6, Währinger Straße 29 1.OG
Monday 16.05. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 20.05. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 23.05. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 27.05. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Seminarraum 6, Währinger Straße 29 1.OG
Monday 30.05. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 03.06. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Friday 10.06. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 13.06. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 17.06. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Seminarraum 6, Währinger Straße 29 1.OG
Monday 20.06. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Friday 24.06. 09:45 - 11:15 Hörsaal III NIG Erdgeschoß
Monday 27.06. 09:45 - 11:15 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7

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
+ pen & paper exercises and their discussion
+ programming exercises

Assessment and permitted materials

Written exam
Programming exercises
Pen & paper exercises (containing small programming tasks; and presentation of the obtainted results; discussion; attendance is mandatory)

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 small programing tasks)

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

87.5% <= P Sehr Gut (1)
75% <= P < 87.5% Gut (2)
62% <= P < 75% Befriedigend (3)
50% <= P < 62% Genügend (4)
0% <= P < 50% Nicht Genügend (5)

At least 30% 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: We 30.03.2022 11:47