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 levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageCzech
Year of introduction2013/2014Year 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
Combined 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 prediction systems at tactical and strategic decision-making level, as well as on modeling based on soft computing techniques and artificial intelligence.

Teaching methods

Lectures
Individual consultations

Summary

The main topics of the course are: - Soft Computing concept. - Mathematical, statistical and probabilistic modeling methods. - Artificial neural networks and forecasting models – 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.

Compulsory literature:

SAUTER, Vicki L. Decision Support Systems for Business Intelligence. Wiley Computer Publishing, 2011. ISBN: 978-0-470-43374-4. ALPAIDYN, Etham. Introduction to Machine Learning (Adaptive Computation and Machine Learning Series), 2010, ISBN: 978-0262012119. MAIMOND, O. and ROKACH, L., editors. Soft Computing for Knowledge Discovery and Data Mining. Springer Verlag, Berlin, Germany, 2007.

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. 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. LUGER, G.F. Artificial Intelligence, Addison Wesley, 2005.

Way of continuous check of knowledge in the course of semester

Zkouška: ústní otázky z daných okruhů

E-learning

Další požadavky na studenta

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

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
2019/2020 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics P Czech Ostrava Choice-compulsory study plan
2019/2020 (P6209) Systems Engineering and Informatics (6209V025) System Engineering and Informatics K Czech Ostrava Choice-compulsory study plan
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