| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2024W |
| 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
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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.
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