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Lecturer(s)
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Burkotová Jana, Mgr. Ph.D.
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Machalová Jitka, doc. RNDr. Ph.D., MBA
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Škorňa Stanislav, Mgr.
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Course content
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1. Polynomial splines. 2. B-splines and their basic properties. 3. Spline interpolation. 4. Splines in least square problem. 5. Smoothing splines. 6. Tensor product splines. 7. Periodic and non-periodic discrete splines. 8. Spline-wavelets. 9. Discrete splines in image and signal processing.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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Gain knowledge about approximation of data by using splines.
Knowledge Gain useful knowledge about approximation of data by using splines.
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Prerequisites
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Standard knowledge from mathematical analysis, linear algebra and numerical methods.
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Assessment methods and criteria
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Oral exam, Seminar Work
Credit: the student has to compute assigned examples. Exam: the student has to understand the subject and be acquainted with theory and computational methods.
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Recommended literature
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C. de Boor. (1978). A Practical Guide to Splines. Springer, New York.
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Ch. Gu. (2013). Smoothing spline ANOVA Models. Springer.
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J. Kobza. (1993). Splajny. skriptum UP, Olomouc.
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K. Najzar. (2006). Základní teorie splinů. skriptum UK, Praha.
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P. Dierckx. (1995). Curve and Surface Fitting with Splines. Oxford University Press, New York.
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