- Lecture Slides
[1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1 .
[2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0 .
Machine Learning
| Language of instruction | angličtina |
|---|---|
| Code | 460-4162 |
| Abbreviation | ML |
| Course title | Machine Learning |
| Coordinating department | Department of Computer Science |
| Course coordinator | prof. Ing. Jan Platoš, Ph.D. |
Summary
The course introduces students to the characteristics of data, its storage, and processing options. Emphasis is placed on data analysis methods, classical machine learning techniques, and modern neural networks, including convolutional and recurrent architectures and autoencoders. Students will learn to interpret and visualize the results obtained and understand when it is appropriate to use individual methods, as well as their principles, assumptions, and expected outputs. Lectures will focus on methodology and principles, while exercises will provide space for practical experiments with real data sets, working with analysis tools, and critical evaluation of the results obtained.
Literature
Advised literature
[1] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
[2] WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, [2017]. ISBN 978-0-12-804291-5.
[3] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333.
[2] WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, [2017]. ISBN 978-0-12-804291-5.
[3] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333.