Syllabus
1. Introduction to the area of adaptive and learning systems
Adaptation and learning, formal description of patterns, selection and ordering of training patterns.
Methods minimizing the loss criterion (Bayesian decision rule, k-nearest neighbor rule, cluster analysis).
Applications of classical learning classifiers (in image recognition, speech processing, control). 2. Artificial neural networks and their application
A brief recapitulation of selected neural network paradigms (feed-forward neural networks of the back-propagation type, Hopfield networks, Kohonen self-organizing maps, deep neural networks).
Applications of neural networks - among others in natural language processing, modeling of financial systems, multimedia data processing, robotics and time series prediction. 3. Application of genetic algorithms in the area of neural networks
Application of multi-layered neural networks of the back-propagation type in the evaluation of the fitness functions for genetic algorithms.
Optimization of the architecture of neural networks by means of genetic algorithms.
Annotation
The course is focused on deeper understanding of the properties and the function of selected models of neural networks - robustness, generalization abilities, etc. Several principles important for the application of neural networks for solving practical tasks will be explained in detail.
The discussed application areas include natural speech processing, image processing, robotics, etc.