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 .
Basics in Artificial Intelligence in GIS
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 548-0135 |
| Abbreviation | AIGIS |
| Course title | Basics in Artificial Intelligence in GIS |
| Coordinating department | Department of Geoinformatics |
| Course coordinator | Ing. Lucie Orlíková, Ph.D. |
Summary
The aim of the course is to introduce students to the fundamentals of neural network theory. The focus is not only on basic theory but also on the ability to apply it in solving practical problems. Students will learn the principles of neural networks, their architecture, and their applications in various fields, particularly in geoinformatics. They will be able to use basic machine learning methods and algorithms, analyze and interpret model results, and work with selected software tools for implementing neural networks. Additionally, they will expand their knowledge of statistics and spatial analysis, which are essential for effectively working with data in the field of artificial intelligence.
Literature
Advised 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 .
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 .