PV021 Neural Networks

Faculty of Informatics
Spring 2009
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
doc. RNDr. Jiří Šíma, DrSc. (lecturer), prof. RNDr. Mojmír Křetínský, CSc. (deputy)
doc. RNDr. Jan Bouda, Ph.D. (assistant)
Lukáš Mojžiš (assistant)
Guaranteed by
prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: prof. RNDr. Mojmír Křetínský, CSc.
Thu 19. 3. 10:00–19:50 B003, Fri 20. 3. 8:00–16:50 B003, Thu 23. 4. 10:00–19:50 B003, Fri 24. 4. 8:00–16:50 C525, Thu 14. 5. 10:00–19:50 B003
Recommended: knowledge corresponding to the courses MB000 (Calculus I) and MB003 (Linear Algebra and Geometry I) or to the courses MB102 (Mathematics II) and MB103 (Mathematics III)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 37 fields of study the course is directly associated with, display
Course objectives
The goal of this course is to begin to understand the foundations of computation performed via Neural Networks. We will concentrate on an Introduction to Neural Networks: motivations, position of neural networks in computer science, review of basic standard models.
  • Introduction to Neural Networks. History of neurocomputing; neurophysiological motivations; mathematical model of neural network: formal neuron, organizational, active, and adaptive dynamics; position of neural networks in computer science: comparison with von Neumann computer architecture, applications, implementations, neurocomputers.
  • Classical Models of Neural Networks. Perceptron: convergence; multi-layered network and backpropagation strategy: choice of topology and generalization; MADALINE: Widrow learning rule.
  • Associative Neural Networks. Linear associative network: Hebb law and pseudohebbian adaptation; Hopfield network: energy, capacity; continuous Hopfield network: traveling salesman problem; Boltzmann machine: simulated annealing, equilibrium.
  • Self-Organization. Kohonen network: unsupervised learning; Kohonen maps: LVQ; counterpropagation: Grossberg learning rule; RBF networks.
  • Seminar: Software implementation of particular neural network models and their simple applications.
  • ŠÍMA, Jiří and Roman NERUDA. Teoretické otázky neuronových sítí. Vyd. 1. Praha: Matfyzpress, 1996, 390 s. ISBN 80-85863-18-9. info
  • KOHONEN, Teuvo. Self-Organizing Maps. Berlin: Springer-Verlag, 1995, 392 pp. Springer Series in Information Sciences 30. ISBN 3-540-58600-8. info
  • HAYKIN, Simon S. Neural Networks : a comprehensive foundation. New York: Macmillan College Publishing Company, 1994, xix, 696. ISBN 0023527617. info
  • Sofsem '88 : sborník referátů : Zotavovna ROH Petr Bezruč, Malenovice, Beskydy 27.11.-9.12.1988. Brno: Ústav výpočetní techniky UJEP Brno, 1988, 363 s. info
Assessment methods
Lectures, class discussion, group projects (4 to 6 people per project). Several midterm progress reports on the respective projects, one final project presentation plus oral examination.
Language of instruction
Further comments (probably available only in Czech)
The course is taught once in two years.
General note: v semestru jaro 2006 se nekona.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Spring 2009, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2009/PV021