155-1305/03 – Soft Computing in Economics (SCE)

Gurantor departmentDepartment of Applied InformaticsCredits6
Subject guarantorprof. Ing. Dušan Marček, CSc.Subject version guarantorprof. Ing. Dušan Marček, CSc.
Study levelundergraduate or graduateRequirementCompulsory
Year1Semesterwinter
Study languageCzech
Year of introduction2018/2019Year 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+2

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
Tutorials
Project work

Summary

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:

SAINI, N. Review of Selection Methods in Genetic Algorithms, International Journal of Engineering and Computer Science, 2017, vol. 6, no. 12, pp. 22261-22263. CHARU C. Aggarwal. Neural Networks and Deep Learning. Springer International Publishing AG, 2018,ISBN 3319944622.

Recommended literature:

CHARU C. Aggarwal. Neural Networks and Deep Learning. Springer International Publishing AG, 2018,ISBN 3319944622. SAINI, N. Review of Selection Methods in Genetic Algorithms, International Journal of Engineering and Computer Science, 2017, vol. 6, no. 12, pp. 22261-22263.

Way of continuous check of knowledge in the course of semester

Credit: - active participation in exercises, submission of the project topic by the 4th week of teaching, - processing of projects according to the required structure and submission in the LMS. - obtaining at least 23 points out of 45. Exam: - written part - test

E-learning

Other requirements

The solution of economical and finacial processes by means artificial neural networks, ART nets and SVM

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: 2020/2021 Winter 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  20
Mandatory attendence parzicipation: Full participation in seminars, completion of project tasks and continuous tests in relation to prescribed topics of lectures.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2020/2021 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner