Course: Method of Artificial Intelligence (Neural Networks)

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Course title Method of Artificial Intelligence (Neural Networks)
Course code KRP/IDZUI
Organizational form of instruction no contact
Level of course not specified
Year of study not specified
Semester Winter and summer
Number of ECTS credits 0
Language of instruction Czech, 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)
Course content
Introduction. Basic terms and definitions. Historical perspective. Biological neural network. Biological neuron. Artificial neural network. Artificial neuron. Percepton. Neural network learning. Training and testing. Artificial neural network classification. Neural network types. Forward, multilayer neural networks. Forward neural network learning. Backpropagation method. Static properties modelling of the systems. Creation of the training and testing matrix. Fuzzy neural networks. Software for neural networks creation. MATLAB/Neural Network Toolbox. Practical examples.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming)
  • unspecified - 20 hours per semester
  • unspecified - 20 hours per semester
  • unspecified - 20 hours per semester
Learning outcomes
The goal of the subject is to teach the students modern methods how to model static and dynamical properties of the system. Students will learn neural network paradigm, theoretical background and practical implementation.
Student will be able to create artificial neural networks and realize computationally their learning and implementation.
Prerequisites
Static and dynamical properties description. Differential and difference equations solution. Basics of continuous- and discrete-time modelling of the processes. Basic knowledge of the computational system MATLAB/Simulink

Assessment methods and criteria
Oral examination, Home assignment evaluation

Seminar lessons attendance. Written seminar work.
Recommended literature
  • ČSN ISO/IEC 2382-34. Informační technologie - Slovník - Část 34: Umělá inteligence - Neuronové sítě. Praha : ČNI, 2001.
  • FAUSETT, L.V. Fundamentals of Neural Network: Architectures, Algorithm and Applications. New Persey : Prentice Hall, 1994.
  • Novák, Mirko . Umělé neuronové sítě : teorie a aplikace. Praha: C.H. Beck, 1998. ISBN 80-7179-132-6.
  • Sinčák, P.; Andrejková, G. Neurónové siete. Inžiniersky prístup. 1. a 2. diel.. Košice : elfa, s.r.o., 1996. ISBN 80-88786-42-88.
  • Šíma, J.; Neruda, R. Teoretické otázky neuronových sítí. Praha : MATFYZPRESS, 1996. ISBN 80-85863-18-9.
  • ŠNOREK, M.; JIŘINA, M. Neuronové sítě a neuropočítače. Praha : ČVUT, 1996.
  • VONDRÁK, I. Umělá inteligence a neuronové sítě. Ostrava: VŠB - TU Ostrava, 1994.


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