Course Unit Code | 548-0135/01 |
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Number of ECTS Credits Allocated | 5 ECTS credits |
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Type of Course Unit * | Compulsory |
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Level of Course Unit * | First Cycle |
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Year of Study * | Third Year |
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Semester when the Course Unit is delivered | Summer Semester |
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Mode of Delivery | Face-to-face |
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Language of Instruction | Czech |
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Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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Name of Lecturer(s) | Personal ID | Name |
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| JUR02 | Ing. Lucie Orlíková, Ph.D. |
Summary |
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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. |
Learning Outcomes of the Course Unit |
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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 |
Course Contents |
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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. |
Recommended or Required Reading |
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Required Reading: |
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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. |
VONDRÁK, Ivo. Umělá inteligence a neuronové sítě: Určeno pro posl. 4. roč. FEI. Ostrava: VŠB-Technická univerzita, 1994. ISBN 80-7078-259-5.
VOŽENÍLEK, Vít, Jiří DVORSKÝ a Dušan HÚSEK, ed. Metody umělé inteligence v geoinformatice. Olomouc: Univerzita Palackého v Olomouci, 2011. ISBN 978-80-244-2945-8.
MAŘÍK, Vladimír, Olga ŠTĚPÁNKOVÁ a Jiří LAŽANSKÝ. Umělá inteligence. Praha: Academia, 1993-. ISBN 80-200-0472-6. |
Recommended Reading: |
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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. |
CHOLLET, François. Deep learning v jazyku Python: knihovny Keras, Tensorflow. Přeložil Rudolf PECINOVSKÝ. Praha: Grada Publishing, 2019. Knihovna programátora (Grada). ISBN 978-80-247-3100-1.
JEŽEK, Josef. Geostatistika a prostorová interpolace. V Praze: Univerzita Karlova, nakladatelství Karolinum, 2015. ISBN 978-80-246-3076-2.
ŘEZANKOVÁ, Hana, Tomáš LÖSTER a Zdeněk ŠULC. Úvod do statistiky. Vydání 2. přepracované. Praha: Oeconomica, nakladatelství VŠE, 2019. ISBN 978-80-245-2301-9. |
Planned learning activities and teaching methods |
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Lectures, Tutorials |
Assesment methods and criteria |
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Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing |
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Credit and Examination | Credit and Examination | 100 (100) | 51 |
Credit | Credit | 33 | 17 |
Examination | Examination | 67 (67) | 18 |
Written part of exam | Written examination | 52 | 18 |
Oral part of exam | Oral examination | 15 | 0 |