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| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students are able to master foundational concepts and techniques of machine learning and data mining.
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| Fertigkeiten |
Kenntnisse |
| (*)Students are familiar with the general data mining process (k3), so that they can identify suitable algorithms for a wide variety of data mining problems (k4), and can competently apply them to these problems (k5). These skills are based on a thorough theoretical understanding of the state-of-the-art in data mining (k5), which enables them to implement such methods on their own (k5). In particular, they are also familiar with the challenges of big data (k4).
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(*)- 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
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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 |
| Sonstige Informationen |
(*)The lecture is accompanied with a voluntary practical course (351.044), in which interested students can collect experience with practical data mining tools such as Weka, KNIME, or RapidMiner.
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