460-4048/01 – Neural Networks (NS)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. Ing. Ivo Vondrák, CSc.Subject version guarantorprof. Ing. Ivo Vondrák, CSc.
Study levelundergraduate or graduateRequirementOptional
Year2Semestersummer
Study languageCzech
Year of introduction2010/2011Year of cancellation2014/2015
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
JEZ04 Ing. David Ježek, Ph.D.
VON05 prof. Ing. Ivo Vondrák, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 8+0

Subject aims expressed by acquired skills and competences

The goal of the course Neural networks is to indroduce trends in paradigm of artificial neural networks.

Teaching methods

Lectures
Seminars

Summary

The goal of the course is to introduce the paradigm of neural networks. The basic model on neuron is introduced as well as architectures of how the artificial neural networks are composed, adapted and used. The fundamental models like multilayered, self-adapting and recurrent neural networks are described.

Compulsory literature:

Mandatory: 1. Hecht-Nielsen: Neurocomputing, Addison-Wesley 1989 2. Wasserman, P.D.: Neural Computing, Theory and Practice. Van Nostrand Reinhold, NY, 1989 Recommended: 3. Rojas, R. Neural Networks: A Systematic Introduction Springer-Verlag New York, Inc., 1996, ISBN: 3-540-60505-3

Recommended literature:

Hecht-Nielsen: Neurocomputing, Addison-Wesley 1989

Way of continuous check of knowledge in the course of semester

Podmínky udělení zápočtu: Samostatný projekt a aktivita na cvičeních

E-learning

Další požadavky na studenta

Additional requirements are placed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron. Multilayered architectures. Backpropagation and its parametric modification. Implemenation of neuron with interval based excitation. Generalized Backpropagation. Recurrent neural networks. Kohonen learning and Self Organized Maps. Counter-propagation. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory. Adaptive Resonance Theory. Object-Oriented Design of Neural Networks. Projects: Design and implementation of a simple perceptron Design and implementation of multilayer neural networks

Conditions for subject completion

Combined form (validity from: 2010/2011 Winter semester, validity until: 2014/2015 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation and Examination Credit and Examination 100 (100) 51
        Exercises evaluation Credit 40 (40) 20
                Semestrální projekt Project 40  20
        Examination Examination 60  30
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2014/2015 (N2647) Information and Communication Technology (1103T031) Computational Mathematics P Czech Ostrava 2 Optional study plan
2014/2015 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Optional study plan
2014/2015 (N2647) Information and Communication Technology (1103T031) Computational Mathematics K Czech Ostrava 2 Optional study plan
2014/2015 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Optional study plan
2013/2014 (N2647) Information and Communication Technology (1103T031) Computational Mathematics P Czech Ostrava 2 Optional study plan
2013/2014 (N2647) Information and Communication Technology (1103T031) Computational Mathematics K Czech Ostrava 2 Optional study plan
2013/2014 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Optional study plan
2013/2014 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Optional study plan
2012/2013 (N2647) Information and Communication Technology (1103T031) Computational Mathematics P Czech Ostrava 2 Optional study plan
2012/2013 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Optional study plan
2012/2013 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Optional study plan
2012/2013 (N2647) Information and Communication Technology (1103T031) Computational Mathematics K Czech Ostrava 2 Optional study plan
2011/2012 (N2647) Information and Communication Technology (1103T031) Computational Mathematics P Czech Ostrava 2 Optional study plan
2011/2012 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Optional study plan
2011/2012 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Optional study plan
2011/2012 (N2647) Information and Communication Technology (1103T031) Computational Mathematics K Czech Ostrava 2 Optional study plan
2010/2011 (N2647) Information and Communication Technology (1103T031) Computational Mathematics P Czech Ostrava 2 Optional study plan
2010/2011 (N2647) Information and Communication Technology (1103T031) Computational Mathematics K Czech Ostrava 2 Optional study plan
2010/2011 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Optional study plan
2010/2011 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner