Basic Mathematical Methods for Imaging and Visualization (IN2124)
Course 0240918393 in WS 2022/3
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Informatics 16 - Chair of Computer Aided Medical Procedures (Prof. Navab) |
| Lecturers |
Tobias Lasser Assistants: Theodor Cheslerean-Boghiu Erdal Pekel |
| Dates |
Wed, 15:30–17:00, MW 1801 Mon, 16:00–18:00, Interims I 102 |
Assignment to Modules
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IN2124: Grundlegende Mathematische Methoden für Imaging und Visualisierung / Basic Mathematical Methods for Imaging and Visualization
This module is included in the following catalogs:- Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
- Catalogue of non-physics elective courses
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | Basic and most commonly applied techniques will be presented in the lectures and demonstrated in example applications from Image Processing and Computer Vision. The same mathematical methods are also applied in other engineering disciplines such as artificial intelligence, machine learning, computer graphics, robotics etc. 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 - Analysis ++ metric spaces and topology ++ convergence, compactness ++ continuity and differentiability in multiple dimension, taylor expansion - Optimization ++ 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. |
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| Links |
Course documents E-Learning course (e. g. Moodle) TUMonline entry |