- Aplikácie inteligentných systémov
- Neural Recommender Systems
- Machine Learning Systems
- Privacy-Preserving Machine Learning
By completing the course, students will gain knowledge about the development and deployment of real-world intelligent system applications. Through lectures, exercises and projects, students will cover the theory, practical knowledge of foundation of generative AI and skills that underpin applied machine learning in terms of building intelligent system applications with respect to data protection. The educational outcomes will provide students with the opportunity to enhance their understanding of machine learning specifics in application domains like e-commerce, monitoring systems, and remote sensing. Working on individual projects will encourage students to come up with their own ideas for implementing intelligent applications in both the development and deployment phases.
- Data and generative foundation of models
- Responsible AI, production-ready application design
- Practical use cases of Applied ML: RecSys, AI-Assisted systems, ML systems
- Data (collection, storage, analysis, recommendation) and generative foundation of models
- Responsible AI, production-ready application design
- Practical use cases
- NGUYEN, G., LYTVYN, O., 2026. Foundations of Generative AI and Applied Machine Learning. Spektrum STU Publishing, ISBN 978-80-227-5556-6, DOI 10.61544/VUEV8972, University Textbook Series, 2026. Available at FIIT STU e-library ELVIRA with AIS access.
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ZHANG A., et al., 2023: Dive into Deep Learning, Cambridge University Press. ISBN 978-1009389433. Adopted at 500 universities from 70 countries.
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AGGARWAL, C.C., 2016. Recommender Systems. Springer. ISBN 978-3-319-29657-9.
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HUYEN, C., 2022. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. O'Reilly Media, Inc. ISBN 978-1098107963.
Knowledge in the course Intelligent Data Analysis (IAU_B) @ FIIT STU at the level of a graduate of the Bachelor's study program in Computer Science is assumed.
