548-0953/02 – GeoComputation (GCC)
| Gurantor department | Department of Geoinformatics | Credits | 10 |
| Subject guarantor | prof. Ing. Igor Ivan, Ph.D. | Subject version guarantor | prof. Ing. Igor Ivan, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2020/2021 | Year of cancellation | |
| Intended for the faculties | HGF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The objective is to develop student’s knowledge of artificial intelligence applied to spatial tasks, regression methods, data mining and dynamics of spatial processes and increase his/her capability to apply geocomputation methods in case studies with topics related to the theme of his/her PhD thesis.
Teaching methods
Lectures
Individual consultations
Other activities
Summary
The course is focused on development knowledge in the field of application of artificial intelligence methods and especially machine learning in geoinformatics, utilization of spatial properties, explanation of classification methods based on machine learning, regression methods based on machine learning, advanced methods for neural networks such as deep learning and convolution neural networks, discrimination analysis, SOM, Bayes networks, logistic regression, symbolic regression, robust regression, data mining methods, including text mining and stream data processing, dynamics of models and basics of dynamics, chaos (transitivity), detection of chaos in geography, fractals, fractal dimension and its application usinf selected algorithms, fractal clustering, self affine fractals and multifractals.
Compulsory literature:
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
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Účast na konzultacích, seminární práce, ústní zkouška.
E-learning
Other requirements
No additional requirements are imposed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
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
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.