[ 921DASICDAK17 ] KV (*)Computational Data Analytics
|
|
|
| Es ist eine neuere Version 2024W dieser LV im Curriculum Masterstudium Artificial Intelligence 2024W vorhanden. |
|
| (*) Leider ist diese Information in Deutsch nicht verfügbar. |
 |
| Workload |
Ausbildungslevel |
Studienfachbereich |
VerantwortlicheR |
Semesterstunden |
Anbietende Uni |
| 3 ECTS |
M1 - Master 1. Jahr |
Informatik |
Johannes Fürnkranz |
2 SSt |
Johannes Kepler Universität Linz |
|
|
|
| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2022W |
| Ziele |
(*)Students master foundational concepts and techniques of machine learning and data mining. They are able to competently use data mining software on practical problems, and have a thorough theoretical understanding, which enables them to implement such methods on their own. In particular, they are also familiar with the challenges of big data.
|
| Lehrinhalte |
(*)- Data mining process models
- Pre-processing techniques
- Inductive rule learning
- Efficient similarity-based techniques
- Clustering for big data
- Association rule mining
- Foundations of Stream Mining
- Evaluation
|
| Beurteilungskriterien |
(*)Written Exam at the end of the semester, Project assignment
|
| Lehrmethoden |
(*)Slide Presentations with Practical Exercises
|
| Abhaltungssprache |
Englisch |
| Literatur |
(*)I. H. Witten, E. Frank, M. A. Hall, C. J. Pal: Data Mining. Morgan Kaufmann.
J. Leskovec, A. Rajaraman, J. D. Ullman: Mining of Massive Datasets. Cambridge University Press.
|
| Lehrinhalte wechselnd? |
Nein |
|
|
|
| Präsenzlehrveranstaltung |
| Teilungsziffer |
- |
| Zuteilungsverfahren |
Direktzuteilung |
|
