460-6006/01 – Neural Networks (NS)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantorprof. Ing. Ivo Vondrák, CSc.Subject version guarantorprof. Ing. Ivo Vondrák, CSc.
Study levelpostgraduate
Study languageCzech
Year of introduction2010/2011Year of cancellation
Intended for the facultiesFS, FEI, HGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
VON05 prof. Ing. Ivo Vondrák, CSc.
ZEL01 prof. Ing. Ivan Zelinka, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Combined Examination 28+0

Subject aims expressed by acquired skills and competences

The goal of the course is to introduce the paradigm of neural networks. The fundamental models of neural networks are described as well the way how they can be implemented and applied.

Teaching methods

Lectures
Individual consultations

Summary

Modern information technologies are very often inspired by biosciences. One of the most important topic is the area of neurocomputing. The idea behind this part of computer science is to find how to model neural network, how to implement them and how to teach them behave in the same or similar way as natural neural networks. Theoretical background of various neural network models is going to be introduced and discussed.

Compulsory literature:

Hecht-Nielsen: Neurocomputing, Addison-Wesley 1989 Wasserman, P.D.: Neural Computing, Theory and Practice. Van Nostrand Reinhold, NY, 1989

Recommended literature:

Hecht-Nielsen: Neurocomputing, Addison-Wesley 1989

Way of continuous check of knowledge in the course of semester

Individual project

E-learning

Další požadavky na studenta

There are not defined other requirements for student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron. 2. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron. 3. Multilayered architectures. Backpropagation and its parametric modification. 4. Implemenation of neuron with interval based excitation. Generalized Backpropagation. 5. Recurrent neural networks. 6. Kohonen learning and Self Organized Maps. Counter-propagation. 7. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory. 8. Využití Hopfieldových sítí v úlohách s omezujícími podmínkami. 9. Adaptive Resonance Theory. 10. Usage of genetic algorithm for neural netwok adaptation. Object-Oriented Design of Neural Networks. Exercises (PC classroom): 1. Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron. 2. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron. 3. Multilayered architectures. Backpropagation and its parametric modification. 4. Implemenation of neuron with interval based excitation. Generalized Backpropagation. 5. Recurrent neural networks. 6. Kohonen learning and Self Organized Maps. Counter-propagation. 7. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory. 8. Využití Hopfieldových sítí v úlohách s omezujícími podmínkami. 9. Adaptive Resonance Theory. 10. Usage of genetic algorithm for neural netwok adaptation. Object-Oriented Design of Neural Networks.

Conditions for subject completion

Full-time form (validity from: 2013/2014 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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Combined form (validity from: 2013/2014 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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