548-0135/01 – Basics in Artificial Intelligence in GIS (AIGIS)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorIng. Lucie Orlíková, Ph.D.Subject version guarantorIng. Lucie Orlíková, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year3Semestersummer
Study languageCzech
Year of introduction2021/2022Year of cancellation
Intended for the facultiesHGFIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
JUR02 Ing. Lucie Orlíková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 8+8

Subject aims expressed by acquired skills and competences

The student demonstrates knowledge of: - fundamental concepts of statistics and geostatistics - fundamental concepts of neural networks - basic Concepts of Python Programming - spatial exploratory data analysis - Basic Concepts of R Programming The student can: - select AI methods and use it for prediction - apply the introduced methods of data processing - interpret the results obtained The student is able to: - orientate in the issue of neural networks - critically interpret foreign solutions based on neural networks - explain the problems of neural networks - choose a suitable method for the given issue

Teaching methods

Lectures
Tutorials

Summary

The aim of the course is to acquaint students with the basics of neural network theory. The student will learn not only the basic theory, but they will be able to solve complex tasks. Students will also expand their knowledge of statistics and spatial analysis.

Compulsory literature:

SULLIVAN, W.: Machine Learning For Beginners: Algorithms, Decision Tree & Random Forest Introduction. Healthy Pragmatic Solutions Inc, 2017. ISBN 978-1975632328. VASILEV, I., SLATER, D., SPACAGNA, G., ROELANTS, P., ZOCCA, V.: Python Deep Learning: Exploring deep learning techniques, neural network architectures and GANs with PyTorch, Keras and TensorFlow. Packt Publishing, 2019. ISBN 978-1-78934-846-0. DENG, N., TIAN, Y., ZHANG, CH.: Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions. Chapman and Hall/CRC, 2012. ISBN 978-1439857922. KANEVSKI, M., POZDNOUKHOV, A., TIMONIN, V.: Machine Learning for Spatial Environmental Data: theory, applications and software, EPFL Press, 2009, 377 p. ISBN 9780429147814.

Recommended literature:

MICHELLUCI, U.: Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection. Apress, 2019. ISBN: 978-1-4842-4976-5. MENSHAWY, A.: Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks. Packt Publishing, 2018. ISBN 1788399900. KANEVSKI, M. (2008): Advanced mapping of environmental data: geostatistics, machine learning and Bayesian maximum entropy. London: ISTE; Hoboken. Geographical information systems series. ISBN 978-1-84821-060-8. GIUSSANI, A. (2020): Applied machine learning with Python. Milano: EGEA Spa - Bocconi University Press. ISBN 978-88-313-2214-0.

Way of continuous check of knowledge in the course of semester

Students are asked about knowledge from areas that they should have already known from previous lectures. They also work on individual tasks. Written and oral exam.

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:

1. Introduction, major topics, context, history, and GIS applications of AI. 2. Exploratory spatial data analysis, introduction to statistical learning theory. 3. Support vector machine: classification and regression, cluster analysis, supervised and unsupervised learning. 4. Decision-trees algorithms: rule learning. 5. Logic and machine learning: specialization, generalization, logical consequence. 6. Verification of learning outcomes: training and test dataset, re-learning, cross-validation, confusion matrices, learning curve. 7. Linear regression, ordinary least square regression modelling. 8. Kernel methods for pattern analysis, kernel transformation. 9. Artificial neural networks: multilayer perceptron, backpropagation method. 10. Cluster analysis: k-nearest neighbours algorithm, hierarchical clustering. 11. Support vector machine. Data preprocessing: selection of attributes, construction of new attributes, sampling methods. 12. Support vector machine. Verification and validation of results. 13. Probabilistic neural network: Bayesian neural network.

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
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67 (67) 18 3
                Written part of exam Written examination 52  18
                Oral part of exam Oral examination 15  0
Mandatory attendence participation: Optional lectures, practice 80%

Show history

Conditions for subject completion and attendance at the exercises within ISP: Lectures by self-study of course materials available at http://geoscience.vsb.cz/. Possibility of personal or on-line consultation. Participation in exercises according to the student's possibilities. To obtain credit, the student must complete a credit project assigned by the instructor no later than the end of the examination period of the semester. The exam must be taken in person.

Show history

Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (B0532A330034) Geoinformatics K Czech Ostrava 3 Compulsory study plan
2024/2025 (B0532A330034) Geoinformatics P Czech Ostrava 3 Compulsory study plan
2023/2024 (B0532A330034) Geoinformatics K Czech Ostrava 3 Compulsory study plan
2023/2024 (B0532A330034) Geoinformatics P Czech Ostrava 3 Compulsory study plan
2022/2023 (B0532A330034) Geoinformatics P Czech Ostrava 3 Compulsory study plan
2022/2023 (B0532A330034) Geoinformatics K Czech Ostrava 3 Compulsory study plan
2021/2022 (B0532A330034) Geoinformatics K Czech Ostrava 3 Compulsory study plan
2021/2022 (B0532A330034) Geoinformatics P Czech Ostrava 3 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

Předmět neobsahuje žádné hodnocení.