548-0083/01 – GeoComputation (GC)
| Gurantor department | Department of Geoinformatics | Credits | 5 |
| Subject guarantor | prof. Ing. Jiří Horák, Dr. | Subject version guarantor | prof. Ing. Jiří Horák, Dr. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory |
| Year | 2 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2010/2011 | Year of cancellation | 2018/2019 |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
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.
Compulsory literature:
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.
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
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:
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.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction