Introduction to the theory of neural networks. Learning process in neural networks
with and without teacher. Neural network as universal approximator. Practical experience with neural networks, classification models, prediction models. Decomposition of a set of objects into training and testing set. Optimal descriptor selection, neural network architecture and number of learning steps. Selected applications of neural networks.