Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Mathematics - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1 module between the following (a.a. 2022/23 Computational Algebra not activated; a.a. 2023/24 Homological Algebra not activated)1 module between the following 3 modules among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Data Fitting and Reconstruction (2022/2023)
Teaching code
4S008269
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/08 - NUMERICAL ANALYSIS
Period
Semester 1 dal Oct 3, 2022 al Jan 27, 2023.
Learning objectives
The course will discuss the theory and practice of approximation of functions and data, in both the univariate and multivariate setting, with an emphasis on splines of various types and interpolation, including subdivsion and other methods for surface reconstruction. A part of the course will be held in a Laboratory setting where some of the techniques presented during the lectures will be implemented in Matlab. At the end of the course the student is expected to be able to demonstrate an in-depth knowledge of the techniques of univariate and multivariate approximation.
Prerequisites and basic notions
Students must have completed an undergraduate Masthematics degree.
Program
The course will discuss the theory and practice of approximation of functions and data, in both the univariate and multivariate setting, with an emphasis on splines of various types and interpolation. A part of the course will be held in a Laboratory setting where some of the techniques presented during the lectures will be implemented in Matlab. At the end of the course the student is expected to able to demonstrate an in-depth knowledge of the techniques of univariate and multivariate approximation. In particular we will study
- the Haar Wavelet
- bivariate Wavelets per images
- Piecewise Linear Splines; theory and appliactions
- Cubic Splines; theory and applications
- Bsplines in general
- Subdivision of spline curves and surfaces
- bivariate Thin Plate Splines
- Positive Definte Functions; applications
- Radial Basis Functions
- compact support RBF
- errors for RBF interpolation
Didactic methods
The lectures will be in-class with videos and pdfs made available at the course Moodle page.
The rights of students will be preserved in situations of travel limitation or confinement due to national provisions to combat COVID or in particular situations of fragile health. In these cases, you are invited to contact the teacher directly to organize the most appropriate remedial strategies
Learning assessment procedures
The exam will be oral.
Due to the present pandemic situation it may be that the exam rules will be adjusted to the situation at hand.
Evaluation criteria
The purpose of the exam is to see if the student is able to recall and reproduce the theory and practice of interpolation and approximation, both univariate and multivariate.
Criteria for the composition of the final grade
The final mark is that of the final exam.
Exam language
O italiano o inglese.
