Allgemeine Angaben 

Basic Mathematical Methods for Imaging and Visualization (IN2124)   




lecture with integrated exercises 






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Allocations: 1  
eLearning[Provide new moodle course in current semester] 



Angaben zur Abhaltung 

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. 


IN0015 Discrete Structures IN0018 Discrete Probability Theory IN0019 Numerical Programming MA0901 Linear Algebra for Informatics MA0902 Analysis for Informatics 


Upon successful completion of the module, participants understand the basic mathematical techniques and methods. They are then able to formulate real problems in the field of imaging and visualization mathematically, and to select methods for solving the problem, to optimize them and to evaluate them. They will also be able to apply these techniques and methods to other engineering disciplines such as artificial intelligence, machine learning, computer graphics, robotics etc. 




The module consists of lectures and tutorial sessions. The content of the lectures is conveyed in presentations of scientific material via slides and blackboard. By solving homework assignments, the students are encouraged to work intensively on the respective topics and their applications. The solutions of the assignments are discussed in the tutorial sessions. 




Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende/r identifizieren. 


Zusatzinformationen 

 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, WellesleyCambridge Press Analysis  Walter Rudin, Real and Complex Analysis, McGrawHill 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  Lloyd Nick Trefethen , Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations  Cleve Moler, chapter 11 of Numerical Computing with MATLAB, SIAM 




Due to the pandemic situation, the course in the winter term 2020/21 is taking place *online* *only*. For more details, please visit the course website. 
