Probabilistic Modelling and Soft Computing Methods

Type of study	Doctoral
Language of instruction	English
Code	155-9507/01
Abbreviation	PMMSC
Course title	Probabilistic Modelling and Soft Computing Methods
Credits	10
Coordinating department	Department of Applied Informatics
Course coordinator	prof. Ing. Dušan Marček, CSc.

Subject syllabus

The main topics of the course are:
- Soft Computing concept.
- Mathematical, statistical and probabilistic modeling methods. Regularization theory applied to modeling of economic processes.
- Artificial neural nets – applications in economics. Neural network learning as a support for model estimates.
- Using data prototype and their emploiment in the development of economic and financial models. Machine learning based on the SVM method (Support Vector Machine).
- Clasification models based on the SVM method and their emploiment for large data modeling.
- Economical time series forecasting using SVM methods – problems and possibilities of their applications.

Literature

Gabor K., Kiss, A. Building Neural Networks as Dataflow Graphs. Proceedings of 2019 IEEE 15th International Scientific Conference on Informatics, Informatics 2019, pp. 216-221, .
KECMAN, Vojislav. Learning and soft computing: support vector machines, neural networks, and fuzzy logic. Massachusetts: The MIT Press, 2001. .
SCHÖLKOPF, B., SMOLA, A. Learning With Kernels. Cambridge, Ma: Mit Press, 2002.

Advised literature

MAIMOND, O. and ROKACH, L., editors. Soft Computing for Knowledge Discovery and Data Mining. Springer Verlag, Berlin, Germany, 2007.
BUHMANN, M.D. Radial Basis Function: Theory and Implementations, Camridge University Press, 2003.
LUGER, G.F. Artificial Intelligence, Addison Wesley, 2005.
MARČEK, Dušan. Supervizované a nesupervizované učení z dat: statistický a soft přístup. SAEI, vol. 45/2016, Ostrava: VŠB-TU Ostrava, 234 s. ISBN 978-80-248-3884-7.

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