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Lecturer(s)
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Mukherjee Amrit, Dr. Ph.D.
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Symonová Radka, doc. Mgr. Ph.D.
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Vohnoutová Marta, Ing.
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Budík Ondřej, Ing.
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Namazi Hamidreza, Dr. Ph.D.
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Bukovský Ivo, doc. Ing. Ph.D.
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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
At least 50 points in a scoring system consisting of: a) Assignments (voluntarily): max. 20 points from voluntary homework assigned during the semester and solved within the set deadline. b) Test for credit (zápočet): max 20 points from the first test before the main test, to obtain credit (zápočet), at least 10 points is necessary to achieve from this test c) Exam (test): max 70 points from the exam Alternatively, a) and b) can be exchanged for an individual semestral project, the topic of which is agreed by the teacher. Thus, during the semester, students can get a maximum of 40 points. Rating: F <50 b, 50 If a student were not active during the semester, i.e., did not solve voluntary homework a) and did not take test b), then the evaluation system shows that they can get the best grade D for the exam. If the student is not able to solve homework during the semester for serious reasons, there is still the possibility of combining a semester project + an exam, and the student can still achieve evaluation A.
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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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