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| Detailed information |
| Original study plan |
Master's programme Computer Science 2025W |
| Learning Outcomes |
Competences |
| Students are able to master foundational concepts and techniques of machine learning and data mining.
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| Skills |
Knowledge |
| 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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| Criteria for evaluation |
Written Exam at the end of the semester
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| Methods |
Slide Presentations with Practical Exercises
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| 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.
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| 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, KNIME, or RapidMiner.
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