Develop the students’ability to design software that is dedicated for solving the difficult problems by exploiting neural computing algorithms.
The course aims to acquire the theoretical and applied knowledge regarding principles of neural calculus
The course aims to acquire the theoretical and applied knowledge regarding design and implementation of neural networks

Artificial intelligence - basic notions

1.     Introduction to neural network theory. Natural Neuron vs Artificial Neuron. Models of neurons and artificial neural networks. Learning in neural networks. Implementations, applications, trends.
2.    Feed-forward neural networks. The Perceptron model.
3.    Multi-feed feed-forward architectures. Limitations of single-level network architectures. Multi-level architectures with feedforward connections.
4.    Radial base function (RBF) networks. Architecture and functioning. Representation capacity of RBF networks. Learning algorithms.
5.    Recurring 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.    Combinatorial optimization problems. Simulated annealing algorithm. Stochastic machines: Boltzmann machines, Helmholtz machines. Applicability and limitations.
7.    Time series processing. Preprocessing. Networks with time windows. The Elman Model.
8.    Cellular networks. Architecture. Operation. Applications in image processing.
9.    Self-organizing neural networks. Unsupervised learning. Biological basics. Self-organizing neural networks (KOHONEN).
10.    Neuro-symbolic hybrid architectures. Extracting rules from neural networks. Expert systems combined with neural networks.
11.    Neuro-fuzzy hybrid architectures. Neuro-genetic hybrid architectures. Genetic algorithms in optimizing neural network topology.
12.    Applications of neural networks. Fields of applicability, examples of known neural systems, successfully used in real problems.

Lecture, conversation, exemplification

The use of computer tools in an interdisciplinary context

- The description of concepts, theories and models used in the application field.

-The identification and explanation of base computer models that are suitable for the application domain.

- The use of computer and mathematical models and tools to solve specific problems in the application field.

- Data and model analysis.

- The development of software components of interdisciplinary projects.

Oral presentation 50% ; Continuous assessment Laboratory activities portfolio 50%

Bishop, Christopher, Pattern Recognition and Machine Learning, Springer-Verlag , New York , 2006 , 150
Haykin, S, Neural Networks: A Comprehensive Foundation, Prentice Hall , New York , 1999 , 200
Rashid, Tariq, Make Your Own Neural Network - A Gentle Journey through the Mathematics of Neural Networks, and Making Your Own Using the Python Computer Language, Prentice Hall , New York , 2016 , 150