1,1,0

Lecture type	Total
Lectures	30

* Load is given in academic hour (1 academic hour = 45 minutes)

The course provides knowledge in theory and applications of artificial neural networks and genetic algorithms. Biological neural networks. Artificial neural networks. Definition. Neuron models. Activation function. Network topologies. Perceptron. Learning laws. Associative networks. Linear associator. Recursive associative networks. Hopfield network. Energy function. Multi-layer networks. Radial-basis function networks. Support vector machine. Delta rule for error back-propagation. Kohonen self-organizing network. K-means clustering algorithm. Boltzmann machine. Simulated annealing. Genetic algorithms. Software for simulation of neural networks. Applications in pattern recognition and signal and image analysis.

1. Predavanja iz neuronskih mreža; S. Lončarić; FER-ZESOI; 2011
2. Neural Networks, 2nd Ed.; S. Haykin; Prentice Hall; 1998; ISBN: 978-0132733502
3. An Introduction to Neural Networks; J. A. Anderson; MIT Press; 1995; ISBN: 978-0262011440

Predmeti specijalizacije profila - profile Information Processing

preporučeni izborni predmeti - profile Computer Engineering
preporučeni izborni predmeti - profile Computer Science
preporučeni izborni predmeti - profile Electronic and Computer Engineering
Predmeti specijalizacije profila - profile Information Processing
preporučeni izborni predmeti - profile Software Engineering and Information Systems
preporučeni izborni predmet - profile Telecommunications and Informatics