UIDI004 Theory and Applications of Artificial Neural Networks

Faculty of Philosophy and Science in Opava
Summer 2015
Extent and Intensity
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
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.
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
Language of instruction
Czech
Further Comments
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%.
The course is also listed under the following terms Winter 2006, Summer 2007, Winter 2007, Summer 2008, Winter 2008, Summer 2009, Winter 2009, Summer 2010, Winter 2010, Summer 2011, Winter 2011, Summer 2012, Winter 2012, Summer 2013, Winter 2013, Summer 2014, Winter 2014, Winter 2015, Summer 2016, Winter 2016, Summer 2017, Winter 2017, Summer 2018, Winter 2018, Summer 2019, Winter 2019, Summer 2020, Winter 2020, Summer 2021, Winter 2021, Summer 2022.
  • Enrolment Statistics (Summer 2015, recent)
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