| Course Unit Code | 548-0083/02 |
|---|
| Number of ECTS Credits Allocated | 5 ECTS credits |
|---|
| Type of Course Unit * | Choice-compulsory |
|---|
| Level of Course Unit * | Second Cycle |
|---|
| Year of Study * | First Year |
|---|
| Semester when the Course Unit is delivered | Summer Semester |
|---|
| Mode of Delivery | Face-to-face |
|---|
| Language of Instruction | English |
|---|
| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
|---|
| Name of Lecturer(s) | Personal ID | Name |
|---|
| HOR10 | prof. Ing. Jiří Horák, Dr. |
| IVA026 | prof. Ing. Igor Ivan, Ph.D. |
| LAM05 | prof. RNDr. Marek Lampart, Ph.D. |
| Summary |
|---|
| 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 |
|---|
| 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 |
|---|
| The course is focused on the introduction to the theory of fuzzy sets and their application in practice. Then, foundations of the theory of decision making in a situation without risk and in a situation with risk are discussed. Second half of the course is devoted to the fractal and chaos theory. |
| Recommended or Required Reading |
|---|
| Required Reading: |
|---|
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: |
|---|
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 |
|---|
| Lectures, Tutorials |
| Assesment methods and criteria |
|---|
| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
|---|
| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 33 | 17 |
| Examination | Examination | 67 (67) | 18 |
| written examination | Written examination | 50 | 18 |
| oral examination | Oral examination | 17 | 0 |