Methods of Experimental Data Processing

Methods of Experimental Data Processing
548-0113/01
čeština Methods of Experimental Data Processing
548-0113/02
angličtina

Summary

The main goal of this course is to introduce to students the basics of artificial neural networks. This course will cover basic neural network architectures and learning algorithms, for applications for environmental data analysis and modelling. The students will have a chance to try out several of these models on practical problems

Literature

HAYKIN, Simon S. Neural networks and learning machines. 3rd ed. Upper Saddle River: Pearson, 2009. 934 s. ISBN 9780131293762.
KOHONEN, Teuvo. Self-Organizing Maps. Berlin: Springer-Verlag, 1995. 392 s. Springer Series in Information Sciences 30. . info
Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. EPFL Press, 2009.
Bishop C. Pattern recognition and machine learning. Springer, 2006.

Advised literature

Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. 2d edition. Springer, 2009.
Kanevski M. (Editor). Advanced Mapping of Environmental Data. Geostatistics, Machine Learning, and Bayesian Maximum Entropy. iSTE/Wiley, 2008.

back to search page

Language of instruction	čeština, angličtina
Code	548-0113
Abbreviation	MEZEK
Course title	Methods of Experimental Data Processing
Coordinating department	Department of Geoinformatics
Course coordinator	Ing. Lucie Orlíková, Ph.D.