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| Detailed information |
| Original study plan |
Master's programme Computer Science 2021S |
| 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, Project assignment
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| Methods |
Slide Presentations with Practical Exercises
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| Language |
English and French |
| 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 |
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