# FI:PV021 Neural Networks - Course Information

## PV021 Neural Networks

**Faculty of Informatics**

Spring 2011

**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. **Timetable**- Fri 8:00–9:50 B410
**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 38 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

- ŠÍMA, Jiří and Roman NERUDA.
**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**

- Enrolment Statistics (Spring 2011, recent)
- Permalink: https://is.muni.cz/course/fi/spring2011/PV021