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Basics in Artificial Intelligence in GIS

Type of study Bachelor
Language of instruction English
Code 548-0135/02
Abbreviation AIGIS
Course title Basics in Artificial Intelligence in GIS
Credits 5
Coordinating department Department of Geoinformatics
Course coordinator Ing. Lucie Orlíková, Ph.D.

Osnova předmětu

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.

Povinná literatura

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 .

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 .


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