Machine Learning
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | English |
| Code | 460-4162/01 |
| Abbreviation | ML |
| Course title | Machine Learning |
| Credits | 5 |
| Coordinating department | Department of Computer Science |
| Course coordinator | prof. Ing. Jan Platoš, Ph.D. |
Osnova předmětu
The main topics covered in the course are:
- Clustering methods and their validation.
- Classification methods and their validation.
- Regression methods and their validation.
- Kernel methods and Support Vector Machines.
- Neural networks, including convolutional and recurrent networks.
- Autoencoders and Variational Autoencoders.
- Signal and time series analysis.
During the exercises, students will test their knowledge using both real and artificial data and apply basic principles.
- Clustering methods and their validation.
- Classification methods and their validation.
- Regression methods and their validation.
- Kernel methods and Support Vector Machines.
- Neural networks, including convolutional and recurrent networks.
- Autoencoders and Variational Autoencoders.
- Signal and time series analysis.
During the exercises, students will test their knowledge using both real and artificial data and apply basic principles.
E-learning
All materials are published on the e-learning portal (https://www.vsb.cz/e-vyuka/en).
Povinná literatura
Doporučená literatura
[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.