| Detailed information |
| Original study plan |
Master's programme Computer Science 2024W |
| Objectives |
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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| Subject |
- 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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| Criteria for evaluation |
Written Exam at the end of the semester
|
| Methods |
Slide Presentations with Practical Exercises
|
| Language |
English |
| Study material |
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.
|
| Changing subject? |
No |
| Further information |
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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