Course: Artificial Neural Networks

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Course title Artificial Neural Networks
Course code AUIUI/AEUNS
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study not specified
Semester Winter and summer
Number of ECTS credits 4
Language of instruction English
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Komínková Oplatková Zuzana, prof. Ing. Ph.D.
  • Janoštík Jakub, Ing.
Course content
- Introduction into neural networks, examples of usage, history of neural nets. - Dividing of neural nets, basic terminology - Training set - preparation - Transfer function, general scheme of neuron - Linear and nonlinear separation of classes, 13. Hilbert's problem, Kolmogorov's theorem - Feedforward nets, Perceptron and its training - Net with algorithm Backpropagation - Hopfield's a CLN net - BAM a Kohonen's net - ART net - Optimization of neural net topology - Usage of neural nets 1 - Usage of neural nets 2

Learning activities and teaching methods
Lecturing, Exercises on PC
  • Participation in classes - 56 hours per semester
prerequisite
Knowledge
Knowledge from areas: Mathematics Fundamentals of Informatics
Knowledge from areas: Mathematics Fundamentals of Informatics
learning outcomes
The student has knowledge about fundamentals of neural nets. The student is well informed in methods of training algorithms of particular types of nets. The student applies correct types of neural nets on given tasks. The student manages to program basic simple training algorithms. The student is able to implement simple applications solved by means of neural nets.
The student has knowledge about fundamentals of neural nets. The student is well informed in methods of training algorithms of particular types of nets. The student applies correct types of neural nets on given tasks. The student manages to program basic simple training algorithms. The student is able to implement simple applications solved by means of neural nets.
teaching methods
Lecturing
Exercises on PC
Lecturing
Exercises on PC
assessment methods
Written examination
Written examination
Recommended literature
  • Bose, N.K., Liang, P. Neural Network Fundamentals with Graphs, Algorithms, and Applications. New York : McGraw-Hill, 1996. ISBN 0-07-006618-3.
  • GOLDBERG, Yoav. Neural network methods for natural language processing. 2017. ISBN 978-1-68173-235-0.
  • Heaton, Jeff. Introduction to neural networks for C#. 2nd ed. St. Louis : Heaton Research, 2008. ISBN 978-1-60439-009-4.
  • Heaton, Jeff. Introduction to neural networks with Java. St. Louis : Heaton Research, 2008. ISBN 978-1-60439-008-7.
  • LAM H-K., LING S. H., NGUYEN H. T. Computational intelligence and its applications: evolutionary computation, fuzzy logic, neural network and support vector machine techniques. 2012. ISBN 978-1-84816-691-2.
  • Onwubolu, Godfrey C. New optimization techniques in engineering. Berlin : Springer, 2004. ISBN 354020167X.
  • Šnorek, M., Jiřina, M. Neuronové sítě a neuropočítače. Praha : ČVUT, 1996. ISBN 80-01-01455-X.
  • ZELINKA, Ivan. Umělá inteligence / kap. 6 Diferenciální evoluce. Praha : Academia, 2003.
  • Zelinka, Ivan. Umělá inteligence : neuronové sítě a genetické algoritmy. 1. vyd. Brno : VUTIUM, 1998. ISBN 8021411635.
  • Zelinka, Ivan. Umělá inteligence v problémech globální optimalizace. 1. vyd. Praha : BEN - technická literatura, 2002. ISBN 8073000695.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester