053621 VU Mining Massive Data (2024S)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 12.02.2024 09:00 to Th 22.02.2024 09:00
- Deregistration possible until Th 14.03.2024 23:59
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
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
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
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
N
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
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 exercises90% <= 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.
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 exercises90% <= 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
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
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 learningMethods:
Lecture
+ pen & paper exercises (~bi-weekly assignments) and their discussion
+ programming exercises