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Lecturer(s)
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Bukovský Ivo, doc. Ing. Ph.D.
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Symonová Radka, doc. Mgr. Ph.D.
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Mukherjee Amrit, Dr. Ph.D.
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Vohnoutová Marta, Ing.
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Budík Ondřej, Ing.
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Goswami Pratik, Ph.D.
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Kulish Vladimír, Ing. DrSc.
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Course content
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Content of lectures: 1. Introduction to Feature Engineering (FA) 2. Feature Engineering for Machine Learning Models 3. Industrial Feature Engineering 4. Advanced Feature Engineering Techniques 5. Fractal and Chaos Theory in Feature Engineering: Nonlinear Dynamics 6. Fractal and Chaos Theory in Feature Engineering 7. Fractal and Chaos Theory in Feature Engineering: Multifractals 8. Feature Engineering for IoT: Part 1 9. Feature Engineering for IoT - 2 10. Feature Engineering for IoT - 3 11. Feature Engineering for Bioinformatics - 1 12. Feature Engineering for Bioinformatics - 2 13. Feature Engineering for Bioinformatics - 3 Content of tutorials/seminar: 1. Introduction to Feature Engineering 2. Feature Engineering for Machine Learning Models 3. Industrial Feature Engineering 4. Advanced Feature Engineering Techniques 5. Fractal and Chaos Theory in Feature Engineering: Nonlinear Dynamics 6. Fractal and Chaos Theory in Feature Engineering 7. Fractal and Chaos Theory in Feature Engineering: Multifractals 8. Feature Engineering for IoT: Part 1 9. Feature Engineering for IoT - 2 10. Feature Engineering for IoT - 3 11. Feature Engineering for Bioinformatics - 1 12. Feature Engineering for Bioinformatics - 2 13. Feature Engineering for Bioinformatics - 3 and location data in
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The course provides students with understanding of feature engineering techniques and their use in various application domains. Students will learn the importance of feature engineering, explore feature selection techniques, and cover industrial, advanced, IoT, and bioinformatics feature engineering. Through lectures, tutorials, and case studies, students will develop the skills to transform raw data into meaningful features for data science.
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Prerequisites
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Undergraduate mathematics (linear calculus), basic programming skills (Python or other)
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Assessment methods and criteria
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unspecified
weekly assignments (optional): 20 points (max) individual semestral project (optional): 20 points (max) combined exam: test 40 points (max), oral exam: 50 points (max) (points and marking F:<50 E:50?59 D:60...69 C:70?79 B:80?89 A: 90? 130)
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Recommended literature
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Edgar, Gerald. Measure, topology, and fractal geometry. 1. vyd. New York : Springer, 1990. ISBN 0-387-97272-2.
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Hendl, Jan. Big data : věda o datech - základy a aplikace. První vydání. Praha : Grada Publishing, 2021. ISBN 978-80-271-3031-3.
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Jianbo Gao, Yinhe Cao, Wen-Wen Tung, Jing Hu. Multiscale analysis of complex time series: integration of chaos and random fractal theory, and beyond, Wiley, 2007.
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MISRA, Sudip, Chandana ROY a Anandarup MUKHERJEE. Introduction to industrial internet of things and industry. ISBN 978-0-367-89758-1.
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R. P. Bonidia, D. S. Domingues, D. S. Sanches, and A. C. P. L. F. De Carvalho. MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors, Briefings in Bioinformatics, vol. 23, no. 1, p. bbab434, Jan. 2022.
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