548-0953/01 – GeoComputation (GCC)

Gurantor departmentDepartment of GeoinformaticsCredits10
Subject guarantorprof. Ing. Igor Ivan, Ph.D.Subject version guarantorprof. Ing. Igor Ivan, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageCzech
Year of introduction2021/2022Year of cancellation
Intended for the facultiesHGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
HOR10 prof. Ing. Jiří Horák, Dr.
IVA026 prof. Ing. Igor Ivan, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Part-time Examination 20+0

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:

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

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

Part-time form (validity from: 2021/2022 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Examination Examination   3
Mandatory attendence participation:

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2023/2024 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2023/2024 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

Předmět neobsahuje žádné hodnocení.