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GeoComputation

Type of study Doctoral
Language of instruction English
Code 548-0953/02
Abbreviation GCC
Course title GeoComputation
Credits 10
Coordinating department Department of Geoinformatics
Course coordinator prof. Ing. Igor Ivan, Ph.D.

Osnova předmětu

Artificial intelligence, basic aproaches, methods.
Machine learning, review of machine learning tasks. Model complexity, loss function, dimenzionality.
Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping.
Introduction to classification. Naive Bayes classification. K-means neighbors algorithm.
Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy.
Support vector machines, regression with SVM (SVR). Discrimination analysis
Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network.
Bayes networks. Bagging, boosting, stacking. Model tuning, model validation
Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning. Text mining. Text preprocessing. Information lift. Weight normalisation.
Logistic regression, symbolic regression, qunatile regression, robust regression
Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters
Data mining from data streams
Model dynamics and dynamic basics. Chaos – tranzitivity. Chaos detection in geography.
Fractals. Fractal dimension and its estimation using selected algorithms.
Fractal clustering, self affine fractals and multifractals

Povinná literatura

AWANGE, J.M., PALÁNCZ, B., LEWIS, R.H., VOLGYESI, L.. Mathematical geosciences. Springer Berlin Heidelberg, New York, NY, 2017.
KANEVSKI M. F., Poudnoukhov A., Timonin V. Machine learning for spatial environmental data. CRC Press 2009. 377 s., 978-0-8493-8237-6
BRAMER, M.A. Principles of data mining. Springer, London, 2020.
ZAKI, M.J., MEIRA, W. Data mining and machine learning: fundamental concepts and algorithms. Cambridge University Press, Cambridge, United Kingdom, 2020; New York, NY

Doporučená literatura

BRUNTON, S.L., KUTZ, J.N. Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press, Cambridge, 2019.
DAUPHINÉ, André. Fractal Geography. Wiley, 2012. ISBN 978-1-84821-328-9.
KANEVSKI M. F., Poudnoukhov A., Timonin V. Machine learning for spatial environmental data. CRC Press 2009. 377 s., 978-0-8493-8237-6
MILLER H. J., HAN J. Geographic Data Mining and Knowledge Discovery. Chapman & Hall/CRC, 2009