155-9507/01 – Probabilistic Modelling and Soft Computing Methods (PMMSC)

Gurantor departmentDepartment of Applied InformaticsCredits10
Subject guarantorprof. Ing. Dušan Marček, CSc.Subject version guarantorprof. Ing. Dušan Marček, CSc.
Study levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2014/2015Year of cancellation
Intended for the facultiesEKFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
HUD0118 doc. Dr. Ing. Miroslav Hudec
MAR0011 prof. Ing. Dušan Marček, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

Subject aims expressed by acquired skills and competences

The course focuses on probabilistic modeling of economic and financial processes with their use in managerial forecasting systems for tactical and strategic decision-making level and also to modeling based on soft computingových techniques and means of artificial intelligence.

Teaching methods

Lectures
Individual consultations

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

Compulsory literature:

KECMAN, Vojislav. Learning and soft computing: support vector machines, neural networks, and fuzzy logic. Massachusetts: The MIT Press, 2001. ISBN 0-262-11255-8. SCHÖLKOPF, B., SMOLA, A. Learning With Kernels. Cambridge, Ma: Mit Press, 2002. HAYKIN, S. Neural Networks: A Comprehensive Foundation. 2nd edition, Prentice Hall, 1998.

Recommended 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.

Way of continuous check of knowledge in the course of semester

Exam: questions from setected tipics

E-learning

Other requirements

Elaborating a written work that has a close relation to the PhD thesis topic.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

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.

Conditions for subject completion

Full-time form (validity from: 2014/2015 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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Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P English Ostrava Choice-compulsory study plan
2020/2021 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K English Ostrava Choice-compulsory study plan
2020/2021 (P0311D050020) Systems Engineering and Informatics K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0311D050020) Systems Engineering and Informatics P English Ostrava Choice-compulsory type B study plan
2019/2020 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P English Ostrava Choice-compulsory study plan
2019/2020 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K English Ostrava Choice-compulsory study plan
2018/2019 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P English Ostrava Choice-compulsory study plan
2018/2019 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K English Ostrava Choice-compulsory study plan
2017/2018 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P English Ostrava Choice-compulsory study plan
2017/2018 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K English Ostrava Choice-compulsory study plan
2016/2017 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P English Ostrava Choice-compulsory study plan
2016/2017 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K English Ostrava Choice-compulsory study plan
2015/2016 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P English Ostrava Choice-compulsory study plan
2015/2016 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K English 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

Occurrence in special blocks

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