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.
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.