PV021 Neural Networks

Faculty of Informatics
Spring 2013
Extent and Intensity
2/0/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
doc. RNDr. Tomáš Brázdil, Ph.D. (lecturer)
RNDr. Jan Krčál, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Tomáš Brázdil, Ph.D.
Supplier department: Department of Computer Science – Faculty of Informatics
Timetable
Fri 12:00–13:50 G123
Prerequisites
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 39 fields of study the course is directly associated with, display
Course objectives
At the end of the course student will have a comprehensive knowledge of neural networks. Will be able to independently learn and explain neural networks problems. Will be able to solve practical problems using neural networks techniques, both independently and as a part of a team. Will be able to critically interpret third party neural-networks based solutions.
Syllabus
  • 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.
  • Project: Software implementation of particular neural network models and their simple applications.
Literature
  • ŠÍMA, Jiří and Roman NERUDA. Teoretické otázky neuronových sítí. Vyd. 1. Praha: Matfyzpress, 1996, 390 s. ISBN 80-85863-18-9. info
  • HAYKIN, Simon S. Neural networks and learning machines. 3rd ed. Upper Saddle River: Pearson, 2009, 934 s. ISBN 9780131293762. info
  • KOHONEN, Teuvo. Self-Organizing Maps. Berlin: Springer-Verlag, 1995, 392 pp. Springer Series in Information Sciences 30. ISBN 3-540-58600-8. info
Teaching methods
Theoretical lectures, group project
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
Czech
Further Comments
Study Materials
The course is taught annually.
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 2009, Spring 2011, Spring 2012, 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 2013, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2013/PV021