155-1305/02 – Soft Computing in Economics (SCE)
| Gurantor department | Department of Applied Informatics | Credits | 4 |
| Subject guarantor | doc. Dr. Ing. Miroslav Hudec | Subject version guarantor | prof. Ing. Dušan Marček, CSc. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2014/2015 | Year of cancellation | 2020/2021 |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
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:
Recommended literature:
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction