| Course Unit Code | 548-0083/04 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| HOR10 | prof. Ing. Jiří Horák, Dr. |
| JUR02 | Ing. Lucie Orlíková, Ph.D. |
| Summary |
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| The subject introduces basic approaches and methods of artificial intelligence, especially machine learning and focus on their utilization in geoinformatics, where it is necessary to evaluate spatial properties, to adapt spatial sampling, and perform appropriate data transformation. Classification methods such as Bayes classifiers, decision trees, support vector machines. Variants for regression analysis. Neural network, including advanced methods such as deep learning and convolution neural network. The further part demonstrates problems and methods of data mining, detection of patterns, sequences and association rule mining, basic techniques of text mining and clustering methods. Introduction to chaos theory and fractals, utlization in geoinformatics. Stochastic spatial simulations. |
| Learning Outcomes of the Course Unit |
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| The objective is to learn student how to use basic methods of artificial intelligence namely machine learning such as decision trees, support vector machines and neural analysis in geoinformatics, explain them pronciples and methods of data mining, theory of chaos and fractals, and selected methods of stochastic spatial simulations. |
| Course Contents |
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1) Artificial intelligence, basic aproaches, methods.
2) Machine learning, basic concepts, supervised learning, unsupervised learning, backpropagation, hybrid methods. Review of machine learning tasks. Model complexity, loss function, dimenzionality.
3) Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping. Preparation of analysis– analysis of sample network, Morisita diagram, data transformation.
4) Introduction to classification. Naive Bayes classification. K-means neighbors algorithm.
5) Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy.
6) Support vector machines, regression with SVM (SVR).
7) Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network.
8) Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning..
9) Text mining. Text preprocessing. Information lift. Weight normalisation.
10) Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters
11) Introduction to chaos and fractals theories. Model dynamics and dynamic basics. Chaos detection in geography.
12) Fractals. Fractal dimension and its estimation using selected algorithms. Application in geoinformatics
13) Introduction to genetic programming. Sworm intelligence. |
| Recommended or Required Reading |
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| Required Reading: |
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AWANGE, J.M., PALÁNCZ, B., LEWIS, R.H., VOLGYESI, L. Mathematical geosciences. Springer Berlin Heidelberg, New York, NY, 2017.
BRAMER, M.A. Principles of data mining. Springer, London, 2020.
KANEVSKI M. F., Poudnoukhov A., Timonin V. Machine learning for spatial environmental data. CRC Press 2009. 377 s., 978-0-8493-8237-6
ZAKI, M.J., MEIRA, W. Data mining and machine learning: fundamental concepts and algorithms. Cambridge University Press, Cambridge, United Kingdom, 2020; New York, NY. |
BRAMER, M.A. Principles of data mining. Springer, London, 2020.
LAMPART, M., HORÁK, J., IVAN, I.: Úvod do dynamických systémů: teorie a praxe v geoinformatice, Vysoká škola báňská – Technická univerzita Ostrava, 2013, ISBN 978-80-248-3185-5.
KANEVSKI M. F., Poudnoukhov A., Timonin V. Machine learning for spatial environmental data. CRC Press 2009. 377 s., 978-0-8493-8237-6
VOŽENÍLEK, V, DVORSKÝ J., HÚSEK D. (ed.) Metody umělé inteligence v geoinformatice. Olomouc: Univerzita Palackého v Olomouci, 2011. ISBN 978-80-244-2945-8. |
| Recommended Reading: |
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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. Advanced mapping of environmental data : geostatistics, machine learning and Bayesian maximum entropy. ISTE 2008. 313 s., 978-1-84821-060-8
MILLER H. J., HAN J. Geographic Data Mining and Knowledge Discovery. Chapman & Hall/CRC, 2009. |
AWANGE, J.M., PALÁNCZ, B., LEWIS, R.H., VOLGYESI, L.. Mathematical geosciences. Springer Berlin Heidelberg, New York, NY, 2017.
ČANDÍK, M., VČELAŘ, F., ZELINKA, I.: Fraktální geometrie - principy a aplikace, BEN-Technická literatura, 2006, ISBN 80-7300-191-8.
KANEVSKI M. F. Advanced mapping of environmental data : geostatistics, machine learning and Bayesian maximum entropy. ISTE 2008. 313 s., 978-1-84821-060-8
ZAKI, M.J., MEIRA, W. Data mining and machine learning: fundamental concepts and algorithms. Cambridge University Press, Cambridge, United Kingdom, 2020; New York, NY. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 33 | 17 |
| Examination | Examination | 67 (67) | 18 |
| písemná zkouška | Written examination | 50 | 18 |
| ústní zkouška | Oral examination | 17 | 0 |