FPF:UIDI004 Theory and Applications of Art - Course Information
UIDI004 Theory and Applications of Artificial Neural Networks
Faculty of Philosophy and Science in OpavaSummer 2021
- Extent and Intensity
- 0/0/0. 0 credit(s). Type of Completion: dzk.
- Guaranteed by
- prof. Ing. Dušan Marček, CSc.
Institute of Computer Science – Faculty of Philosophy and Science in Opava - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Autonomous Systems (programme FPF, P1801 Inf) (2)
- Autonomous Systems (programme FPF, P1801 Inf) (2)
- Course objectives
- The course focuses on Artificial Neural Networks (ANN) technology for their utilization and application in enterprices and institutions. The aim is to understand the use of ANN technology, their theoretical background practical design and deployments. The course of ANN builds on mathematical aspects of behavior of neural cells in living organisms to solve various kinds of problems from the AI area, which are frequently applied in tasks like data mining, expert/knowledge systems, trend predictions etc. The result is a sequence of neural algorithms capable to learn from examples, to generalize knowledge and to search for approximate solutions of various types of problems including intractable ones. The neural algorithms can be run on special parallel machines but nowadays most often on classical computers.
- Syllabus
- 1. The structure of biological neuron, mathematical model of a simple neuron and a multi-layer neural network. Features and applications of artificial neural networks.
2. Active, adaptive and organization dynamics, neural training schemes (supervised/unsupervised/reinforcement). Training and testing sets, training process, the overfitting problem.
3. The perceptron and its training algorithm. Implementation of simple logic functions. Limited capabilities of single-layer perceptron.
4. Multilayer networks and the Backpropagation (BP) algorithm. Modifications and improvements of the BP algorithm (training speed adjustment, the momentum term, gain adaptation).
5. Efficient methods for training of the multilayer perceptron: conjugate-gradient methods, resilient propagation, further methods.
6. Hetero- and auto-associative networks, topology and training, synchronous and asynchronous models. The Hopfield model, stability and energy, storage capacity
7. Radial Basis Function networks, organization and active dynamics. Three phases of training, properties, applications, a comparison with multilayer perceptron.
8. Competitive networks, the vector quantization problem, Lloyd's algorithm. The Kohonen training rule, the UCL, DCL a SCL variants of training.
9. Self-organizing maps - SOM, description and applications, the neighbourhood function, examples of single- and two-dimensional maps.
10. The ART networks, principles and properties, the vigilance function.
- 1. The structure of biological neuron, mathematical model of a simple neuron and a multi-layer neural network. Features and applications of artificial neural networks.
- Literature
- recommended literature
- Haykin, S. Kalman Filtering and Neural Networks. NY: John Wiley and Sons, 2002. info
- Kecman, V. Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Massachusetts Institute of Technology, The MIT P, 2001. info
- Hassoun, M.H. Fundamentals ofArtificial Neural Networks. The MIT Press, Cambridge, Messachusetts,London, 1994. info
- Hertz, J.; Krogh, A.; Palmer, R., G. Introduction to the Theory of Neural Computation. Addison-esley, 1991. info
- Teaching methods
- Interactive lecture
Lecture with a video analysis - Assessment methods
- Exam
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course can also be completed outside the examination period.
- Teacher's information
- A study of selected topics of parallel implementation of autonomous systems due to teacher's recommendation related to the PhD thesis of the student, an oral exam - success rate 50%.
- Enrolment Statistics (Summer 2021, recent)
- Permalink: https://is.slu.cz/course/fpf/summer2021/UIDI004