| Course Unit Code | 050-0541/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Choice-compulsory type B |
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| Level of Course Unit * | First Cycle |
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| Year of Study * | First 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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| SEN76 | doc. Ing. Pavel Šenovský, Ph.D. |
| Summary |
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| The students will learn basic principles of the function and deployment of various methods of artificial intelligence. The focus of the study will be into applications in safety and security. In practical part of the course, the students will gain first hand experience with design and usage of neural networks to solve practical problems. |
| Learning Outcomes of the Course Unit |
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| Students will gain basic knowledge for usage of artificial intelligence (foremost neural networks) to solve practical problems. |
| Course Contents |
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1. Introduction – Short History
2. Generative AI and road to AGI
3-4. Introduction to Object Analysis using UML Language
5. Expert Systems
6. Neural Networks and its Adaptation
7. Prediction and its Usage
8. Neural Networks for Classification Problems
9. Neural Networks for Image Recognition
10. Case Studies of Neural Networks Usage
11. Cellular Automata
12. Multi-Agent Systems
13. Genetic Algorithms |
| Recommended or Required Reading |
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| Required Reading: |
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Russell, S.J., Norvig, P. Artificial intelligence: a modern approach. 3rd ed., Pearson new international ed. Harlow: Pearson, c2014. Pearson custom library. ISBN 978-1-292-02420-2.
R: The R Project for Statistical Computing [online]. [cit. 2018-09-4]. Dostupné z: https://www.r-project.org/ |
Šenovský, P. Umělá inteligence v bezpečnosti. Ostrava: VŠB-TU Ostrava, 2019, 123 s., dostupné z https://fbiweb.vsb.cz/~sen76/data/uploads/skripta/ai.pdf [cit. 2019-09-06]
R: The R Project for Statistical Computing [online]. [cit. 2018-09-4]. Dostupné z: https://www.r-project.org/ |
| Recommended Reading: |
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Haykin, S.S. Neural networks and learning machines. 3rd ed., Pearson international ed. Upper Saddle River: Pearson, c2009. ISBN 978-0-13-129376-2.
Dreyfus, G. Neural networks: methodology and applications. Berlin: Springer, c2005. ISBN 3-540-22980-9. |
Mařík, V., Štěpánková, O., Lažanský, J. a kol: Umělá inteligence (1). Academia, Praha 1993, 264 str., ISBN 80-200-0496-3.
Mařík, V., Štěpánková, O., Lažanský, J. a kol: Umělá inteligence (2). Academia, Praha 1997, 373 str., ISBN 80-200-0504-8.
Mařík, V., Štěpánková, O., Lažanský, J. a kol: Umělá inteligence (3). Academia, Praha 2001, 328 str., ISBN 80-200-0472-6. |
| 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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| Graded credit | Graded credit | 100 | 51 |