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Basics in Artificial Intelligence in GIS

Type of study Bachelor
Language of instruction Czech
Code 548-0135/01
Abbreviation AIGIS
Course title Basics in Artificial Intelligence in GIS
Credits 5
Coordinating department Department of Geoinformatics
Course coordinator Ing. Lucie Orlíková, Ph.D.

Subject syllabus

1. Introduction to Artificial Intelligence (AI): definition of concepts, historical overview, successful applications in geoinformatics.
2. Exploratory data analysis: review of statistical foundations and their importance for machine learning.
3. Fundamentals of Machine Learning (ML): classification and regression, cluster analysis, supervised and unsupervised learning, illustrative examples.
4. Decision trees in AI and ML: principles of decision tree learning and their use in predictive analysis.
5. Logic and machine learning: specialization, generalization, logical consequence in AI.
6. Validation of machine learning results: training and test sets, overfitting, cross-validation, confusion matrix, learning curve.
7. Linear regression in ML: least squares method and its use in classification and prediction.
8. Kernel methods in ML: principles of kernel transformation and their applications in machine learning.
9. Neural networks in AI: multi-layer neural networks, backpropagation, and deep learning.
10. Cluster analysis in ML: k-nearest neighbors algorithm, hierarchical clustering, and their applications in geoinformatics.
11. Practical machine learning: data preprocessing, feature selection, feature engineering, sampling methods.
12. Verification and validation of ML models: evaluation of accuracy and reliability of predictive models.
13. Modern artificial intelligence and its applications: language models such as ChatGPT and their uses, generative AI, task automation in geoinformatics and other disciplines, ethical aspects of AI.

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 .

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 .