155-1305/01 – Soft Computing in Economics (SCE)

Gurantor departmentDepartment of Computer ScienceCredits5
Subject guarantorprof. Ing. Dušan Marček, CSc.Subject version guarantorprof. Ing. Dušan Marček, CSc.
Study levelundergraduate or graduateRequirementCompulsory
Year1Semestersummer
Study languageCzech
Year of introduction2013/2014Year of cancellation
Intended for the facultiesEKFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
MAD0032 Ing. Martin Maděra
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

Lectures
Individual consultations

Summary

The course focuses on soft computing (SC) methods such as neural networks (NNs), machine learning for modeling and forecasting of the large class of non linear and time unstable economic processes and chaotic financial systems. The aim is to understand the use of SC methods in economics. It explains the development of economic models based on SC methods and their deployment, offers an understanding of NN architectures and understanding of their learning. It discuses and assesses the performance of intelligent information processing.

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

E-learning

Další požadavky na studenta

NNs and SVM approaches of economic and financial problems solving.

Prerequisities

Subject has no prerequisities.

Co-requisities

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: 2013/2014 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation and Examination Credit and Examination 100 (100) 51
        Exercises evaluation Credit 45  25
        Examination Examination 55  26
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N6209) Systems Engineering and Informatics (6209T017) Informatics in Economics P Czech Ostrava 1 Compulsory study plan
2018/2019 (N6209) Systems Engineering and Informatics (6209T017) Informatics in Economics P Czech Ostrava 1 Compulsory study plan
2017/2018 (N6209) Systems Engineering and Informatics (6209T017) Informatics in Economics P Czech Ostrava 1 Compulsory study plan
2016/2017 (N6209) Systems Engineering and Informatics (6209T017) Informatics in Economics P Czech Ostrava 1 Compulsory study plan
2015/2016 (N6209) Systems Engineering and Informatics (6209T017) Informatics in Economics P Czech Ostrava 1 Compulsory study plan
2014/2015 (N6209) Systems Engineering and Informatics (6209T025) System Engineering and Informatics P Czech Ostrava 1 Compulsory study plan
2013/2014 (N6209) Systems Engineering and Informatics (6209T025) System Engineering and Informatics P Czech Ostrava 1 Compulsory study plan
2013/2014 (N6209) Systems Engineering and Informatics (6209T025) System Engineering and Informatics (00) 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