Probabilistic Modelling and Soft Computing Methods

Probabilistic Modelling and Soft Computing Methods
155-9507/01
angličtina

Summary

Soft Computing concept. Mathematical, statistical and probabilistic modelling methods. Regularization theory applied to modelling of economic processes. Artificial neural nets – applications in economics. Neural network learning as a support for model estimates. Using data prototype and their employment in the development of economic and financial models. Machine learning based on the SVM method (Support Vector Machine). Classification models based on the SVM method and their employment for large data modelling. 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.

back to search page

Language of instruction	angličtina
Code	155-9507
Abbreviation	PMMSC
Course title	Probabilistic Modelling and Soft Computing Methods
Coordinating department	Department of Applied Informatics
Course coordinator	prof. Ing. Dušan Marček, CSc.