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. ISBN 3-540-58600-8 . 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.
Methods of Experimental Data Processing
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 548-0113 |
| Abbreviation | MEZEK |
| Course title | Methods of Experimental Data Processing |
| Coordinating department | Department of Geoinformatics |
| Course coordinator | Ing. Lucie Orlíková, Ph.D. |
Anotace
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
Povinná literatura
Doporučená literatura
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.
Kanevski M. (Editor). Advanced Mapping of Environmental Data. Geostatistics, Machine Learning, and Bayesian Maximum Entropy. iSTE/Wiley, 2008.