[ 921DASICDAK17 ] KV (*)Computational Data Analytics
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| Es ist eine neuere Version 2025W dieser LV im Curriculum Masterstudium Artificial Intelligence 2025W vorhanden. |
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| (*) Leider ist diese Information in Deutsch nicht verfügbar. |
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| Workload |
Ausbildungslevel |
Studienfachbereich |
VerantwortlicheR |
Semesterstunden |
Anbietende Uni |
| 3 ECTS |
M1 - Master 1. Jahr |
Informatik |
Johannes Fürnkranz |
2 SSt |
Johannes Kepler Universität Linz |
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| 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.
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| 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
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| Beurteilungskriterien |
(*)Written Exam at the end of the semester, Project assignment
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| Lehrmethoden |
(*)Slide Presentations with Practical Exercises
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| 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.
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| Lehrinhalte wechselnd? |
Nein |
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| Präsenzlehrveranstaltung |
| Teilungsziffer |
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| Zuteilungsverfahren |
Direktzuteilung |
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