DISPALY OVERWIEW:

Neural Nets and Neurocomputers (X36NAN)
course in Czech language

full-time study course, currently not teaching
Number of teaching periods (lectures + seminars): 2+2
Termination: Credit, examination

  Summary:  

  Mainly the basic neural paradigms are studied (perceptron and perceptron-like artificial neural nets, Hopfield net, Kohonen and ART selforganizing nets, Neocognitron, GMDH, etc.). Their applications in different tasks are pointed out. Fundamental ideas of HW accelerator design are mentioned. Applications like data prediction, image and sound neural processing, data compression, principal and independent component analysis are described.

  Course Syllabus:  
 
  • Artificial neural nets introduction
  • Hopfield net
  • Perceptron-like neural nets
  • Back-propagation nets, the principle of error back propagation
  • Self-organizing nets - Kohonen
  • Self-organizing nets - ART
  • GMDH nets
  • Neocognitron
  • Image processing by artificial neural nets
  • Data prediction
  • Data mining
  • Neural net accelerators
  • Boltzman machine
  • Fuzzy neural nets

  Seminar syllabus:  
 
  • Examples of artificial neural net application
  • Simulation tools
  • 1st laboratory task analysis
  • Consultations
  • Result presentation
  • 2nd laboratory task analysis
  • Consultations
  • Result presentation
  • Evaluation

  Literature:  
 
  • Haykin, S.: Neural Networks. IEEE Computer Society Press 1994


  Teachers:  
  Lecturer:
Seminar leaders:

Department of Computer Science and Engineering

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