Lecturer(s)


Beránek Ladislav, doc. Ing. CSc.

Course content

1. Introduction to the information theory and information science 2. Information science, the subject of investigation 3. Formalization in information theory 4. Signal, communication, communications chain 5. Information encoding 6. Turing machines 7. Algorithmic solvability of problems 8. Introduction, biological neuronal networks, definition of artificial neuronal networks 9. Linear associative memory 10. Perceptron 11. Hopfield network 12. Selforganizing maps 13. Backpropagation models 14. Application of neural networks

Learning activities and teaching methods

Monologic (reading, lecture, briefing)

Learning outcomes

The aim of the subject is to provide students with the basic concepts and methods of information theory and some fundamental conceptions of neural networks. The following topics will be discussed  information theory, encoding, data compression, measure of information, mean information content, elements of neural computers theory and algorithms and construction and neural network learning.
Students will have a view in particular topics of theoretical computer science and will be able to decide what method to use at a concrete problem solving.

Prerequisites

Subjects Mathematics (MATA) and Discrete mathematics I. (DIM1)

Assessment methods and criteria

Written examination
Working out a seminar work deepening the chosen topic (description of other type of neural network, coding theory etc.), fulfillment of a written test.

Recommended literature


Herz a kol. Introduction to the Theory of Neural Compucation..

Křivan, M. Umělé neuronové sítě. Skripta VŠE.. Praha, 1995.

Vaníček, J., Papík, M. a kolektiv. Teoretické základy informatiky.. Praha: Alfa, 2007.
