Neural Networks and Applications

1. Introduction to the theory of neural networks. Natural neuron and artificial neuron. Models of neurons and artificial neural networks. Learning neural networks. Implementations, applications, trends. 2. Feed-forward neural networks. The Perceptron Model. Adaline and Madaline models. Delta rule. 3. Multilayer feed-forward architectures and error retropropagation method. Limits of one-tier networks. Multi-level architectures with feedforward connections. 4. Radial Basic Function Networks (RBF). Architecture and operation. The ability to represent RBF networks. Learning algorithms 5. Recurrent neural networks for associative memories. Associative memories. A mathematical model of the recurrent neural network. Hopfield model and data storage algorithms (Hebb rule, Diederich-Opper algorithm). 6. Problems of combinatorial optimization. Simulated riding algorithm. Stochastic machines: Boltzmann machine, Cauchy machine, Helmholtz machine. Applicability and limitations. 7. Processing of time series. Preprocessing. Networks with temporary windows. The Elman model. 8. Cellular networks. Architecture. Operation. Applications in image processing. 9. Self-organizing neural networks. Unsupervised learning. Biological basis. Self-organizing neural networks, 10. Neuro-symbolic hybrid architectures. Extraction of rules from neural networks. Expert systems combined with neural networks. 11. Neuro-fuzzy hybrid architectures. Neuro-genetic hybrid architectures. Genetic algorithms for optimizing the topology of a neural network. 12. Applications of neural networks. Areas of applicability, examples of known neural systems, successfully used in real problems.

Upon completing this course, the student will be able to apply the main machine learning techniques to solving practical problems. Advanced students will be able to use these techniques in research.