Basic Mathematical Methods for Imaging and Visualization
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2124 is a semester module in English language at Bachelor’s level and Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
The module IN2124 is covering topics such as:
- Linear Algebra
++ linear spaces and bases
++ linear mappings and matrices
++ linear equation systems, solving linear equation systems
++ least squares problems
++ eigen value problems and singular value decomposition
++ metric spaces and topology
++ convergence, compactness
++ continuity and differentiability in multiple dimension, taylor expansion
++ existence and uniqueness of minimizers, identification of minimizers
++ gradient descent, conjugate gradient
++ Newton method, fixed point iteration
- Probability theory
++ probability spaces, random variables
++ expectation and conditional expectation
++ estimators, expectation maximization method
In the exercises the participants have the opportunity to gain deeper understanding and to collect practical experience while implementing or applying the methods in order to solve real problems, .
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||4||Basic Mathematical Methods for Imaging and Visualization (IN2124)||
Assistants: Cheslerean-Boghiu, T.Page Vizcaino, J.Pekel, E.Wollek, A.
Wed, 15:30–17:00, MW 1801
Mon, 16:00–18:00, Interims I 102
Learning and Teaching Methods
- Cleve Moler, first chapter of Numerical Computing with MATLAB, SIAM Linear Algebra
- Yousef Saad, Iterative Methods for Sparse Linear Systems, SIAM
- Lloyd N. Trefethen and David Bau, Numerical Linear Algebra, SIAM
- Gilbert Strang, Introduction to Linear Algebra, Wellesley-Cambridge Press Analysis
- Walter Rudin, Real and Complex Analysis, McGraw-Hill Optimization
- Ake Björck, Numerical Methods for Least Squares Problems, SIAM
- Jonathan Shewchuk, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
- Uri Ascher, A first course in numerical methods, SIAM Probability Theory
- Heinz Bauer, Measure and Integration Theory, deGruyter
- Sheldon Ross, Introduction to probability and statistics for engineers and scientists, Elsevier PDEs
- Lloyd Nick Trefethen , Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations
- Cleve Moler, chapter 11 of Numerical Computing with MATLAB, SIAM
Description of exams and course work
The exam takes the form of a 75-minute written test, in which the students, based on the questions posed, are intended to demonstrate their knowledge of the basic mathematical methods as well as their ability to apply those methods successfully when solving basic abstract mathematical problems. In addition, by answering questions about concrete applications in image processing and computer vision, the students are expected to show that they can formulate applied problems mathematically, that they can analyze their mathematical properties, and that they can solve them using suitable methods.
The exam may be repeated at the end of the semester.