155-1305/02 – Soft Computing in Economics (SCE)

Gurantor departmentDepartment of Applied InformaticsCredits4
Subject guarantorprof. Ing. Dušan Marček, CSc.Subject version guarantorprof. Ing. Dušan Marček, CSc.
Study levelundergraduate or graduate
Study languageCzech
Year of introduction2014/2015Year of cancellation
Intended for the facultiesEKFIntended for study typesFollow-up Master
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 Credit and Examination 2+1

Subject aims expressed by acquired skills and competences

1. To gain a basic knowledge of SC information technologies 2. To understand the role and application of supervised and unsupervised learning 3. To understand the architectures of NNs building for economic applications 4. To understand the role of SOM NNs and applications in decision making 5. To learn the issues on SVM learning

Teaching methods

Project work


The aim of the course is to understand and use stochastic and intelligent SC methods in economics for modeling and construction of flash predictions for economic and financial processes. These methods are based on supervised, unsupervised and hybrid learning from data, development of novel ANN architectures and design of novel systems for business applications. Students will be able to discuss and evaluate the performance of intelligent information processing in comparison with probabilistic computation.

Compulsory literature:

HERTZ, J., KROGH, A. a R. G. PALMER. Introduction to the Theory of Neural Computation. Addison-esley, 1991, ISBN 978-0201515602 SUYKENS, Johan, A. K., VANDEWALLE, Joos, P.L., de MOOR, B.L. Artificial Neural Networks for Modeling and Control of Non-Linear systems, Springer-Verlag, 1995, ISBN 9780262514675

Recommended literature:

CHARU C. Aggarwal. Neural Networks and Deep Learning. Springer International Publishing AG, 2018,ISBN 3319944622.

Way of continuous check of knowledge in the course of semester


Other requirements

NNs and SVM approaches of economic and financial problems solving.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Introduction to NNs and SC, mathematical model, basic learning principles. 2. Single-layer networks, perceptron – learning rule, adaptation of linear neuron. 3. Multilayer perceptrons, architectures, Backpropagation algorithms. 4. Modeling and forecasting of economic/financial time series using multilayer perceptrons. 5. Associative memories, applications to economic issues solving. 6. Recurrent NNs, RTL learning, applications to economic dynamic systems. 7. RBF NNs, architectures, learning methods. 8. NNs with unsupervised learning, competitive learning – relation ship to data mining. 9. Self organizing maps – SOM NNs, architectures, learning, applications in decision making. 10. Hybrid NNs, architecture, learning. 11. The main steps in the formulation of NNs, applications in economics and finance. 12. Machine learning, applications to data classification. 13. Regression models by support Vector Machines (SVM), application to financial high frequency time series. 14. Granular Computing (GC), principles, cloud concept, current trends in the context of probabilistic vs. intelligent (soft) computing.

Conditions for subject completion

Full-time form (validity from: 2015/2016 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  25
        Examination Examination 55  26
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2015/2016 (N6209) Systems Engineering and Informatics (6209T025) System Engineering and Informatics P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
Subject block without study plan - EKF - P - cs 2016/2017 Full-time Czech Optional EKF - Faculty of Economics stu. block