155-0907/01 – Probabilistic Modelling and Soft Computing Methods (PMMSC)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantorprof. Ing. Dušan Marček, CSc.Subject version guarantorprof. Ing. Dušan Marček, CSc.
Study levelpostgraduate
Study languageCzech
Year of introduction2013/2014Year of cancellation
Intended for the facultiesEKFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
MAR0011 prof. Ing. Dušan Marček, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 30+0
Combined Examination 30+0

Subject aims expressed by acquired skills and competences

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.

Teaching methods

Lectures

Summary

Předmět se soustřeďuje na pravděpodobnostní modelování ekonomických a finančních procesů s jejich využitím v manažérských predikčních systémech na taktické a strategické úrovni rozhodování a též na modelováni založeném na soft computingových technikách a prostředky umělé inteligence.

Compulsory literature:

SCHÖLKOPF, B., SMOLA, A. Learning With Kernels. Cambridge, Ma: Mit Press, 2002. HAYKIN, S. Neural Networks: A Comprehensive Foundation. 2nd edition, Prentice Hall, 1998. MAIMOND, O. and ROKACH, L., editors. Soft Computing for Knowledge Discovery and Data Mining. Springer Verlag, Berlin, Germany, 2007. JIN, B., ZHANG, I.Q and WANG, B.H. Granular Kernel Trees with Parallel Genetic Algorithms for Drug Activity Comparisons, International Journal of Datamining and Bioinformatics, vol. 1, no 3, pp. 270-285, 2007. MARCEK, M., MARCEK, D. Granular RBF Neural Network Implementation of Fuzzy Systems: Application to Time Series Modelling. Journal of Multi-Valued Logic & Soft Compiting, 14 (2008), pp.400-414. ALPAIDIN, E. Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, 2004. ALPAIDYN, E. Introduction to Machine Learning, Cambridge, Mass.: MIT Press, 2010. BUHMANN, M.D. Radial Basis Function: Theory and Implementations, Camridge University Press, 2003. LUGER, G.F. Artificial Intelligence, Addison Wesley, 2005.

Recommended literature:

SCHÖLKOPF, B., SMOLA, A. Learning With Kernels. Cambridge, Ma: Mit Press, 2002. HAYKIN, S. Neural Networks: A Comprehensive Foundation. 2nd edition, Prentice Hall, 1998. MAIMOND, O. and ROKACH, L., editors. Soft Computing for Knowledge Discovery and Data Mining. Springer Verlag, Berlin, Germany, 2007. JIN, B., ZHANG, I.Q and WANG, B.H. Granular Kernel Trees with Parallel Genetic Algorithms for Drug Activity Comparisons, International Journal of Datamining and Bioinformatics, vol. 1, no 3, pp. 270-285, 2007. MARCEK, M., MARCEK, D. Granular RBF Neural Network Implementation of Fuzzy Systems: Application to Time Series Modelling. Journal of Multi-Valued Logic & Soft Compiting, 14 (2008), pp.400-414. ALPAIDIN, E. Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, 2004. ALPAIDYN, E. Introduction to Machine Learning, Cambridge, Mass.: MIT Press, 2010. BUHMANN, M.D. Radial Basis Function: Theory and Implementations, Camridge University Press, 2003. LUGER, G.F. Artificial Intelligence, Addison Wesley, 2005.

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

no further requirements

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

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.

Conditions for subject completion

Full-time form (validity from: 2013/2014 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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Combined form (validity from: 2013/2014 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2018/2019 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2018/2019 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan
2017/2018 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2017/2018 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan
2016/2017 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2016/2017 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan
2015/2016 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2015/2016 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan
2014/2015 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2014/2015 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan
2013/2014 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2013/2014 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner