Applied Image and Signal Processing
Analytics and Knowledge Discovery (FHS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AISM1AKDIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The module Analytics and Knowledge Discovery leads students to classical approaches on Exploratory Data Analysis for data with different kind of representation (numerical, categorical, text). For implementing a knowledge discovery process, they apply methods to reduce the dimension-ality of data, cluster it and apply various visualization methods. The course concentrates on unsupervised methodology.
Superior module:
Data Science & Analytics
Module description:
xxx
Data Science (FHS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AISM1DSCIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Upon completion of this course, students know about types and ingredients of data science projects, entitle their structure and identify different types of team members. They understand the concepts of data, models and algorithms and use specific language to describe data. They discuss the appropriateness of a data collection or intended data acquisition process with respect to a data science or artificial intelligence project. Students are introduced to the classical approach for extracting information from data with different kind of representation (numerical, categorial, one-hot or text). They collect, pre-process and visualize this data to gain basic data understand-ing. They follow the design cycle for supervised methodology by implementing data-specific feature generation, sampling of training and testing data, training selected (simple) classifiers and evaluating their performance. The students use state-of-the-art development tools and scalable technology and argue their approach content-wise.
Superior module:
Data Science & Analytics
Module description:
xxx
Digital Signal Processing 1 (FHS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AISM1DSPIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Students understand the basic mathematical concepts to describe continuous and discrete time signals and systems and know the relations between time and frequency domain. They are famil-iar with the foundations of signal sampling and discretization and can apply important transfor-mations, e.g. Fourier-, Laplace and z-transformation. They understand basic algorithms in digital signal processing like FFT, convolution and correlation. They can transform continuous to discrete time systems e. g. with help of the impulse invariant or bilinear transformation and understand the restrictions. They have profound knowledge in designing and implementing digital filters and are also familiar with their applications. Students also have experience in simulation of DSP algorithms in a lab environment and are able to implement discrete systems with help of simulation software and low-level programming languages.
Superior module:
Digital Signal Processing
Module description:
xxx
Image Processing and Imaging (PLUS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students are able to understand the difference of varying imaging sensor devices and have knowledge about fundamental algorithms and procedures in spatial as well as transform-domain image processing and computer vision with an emphasis on segmen-tation and image filtering.
Superior module:
Visual Data Processing & Representation
Module description:
xxx
Image Processing and Imaging (PLUS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
Students have first experiences in usage of image processing and vision libraries and toolboxes and are able to apply their knowledge in focused projects, also programming own code related to the tasks defined in the programming projects.
Superior module:
Visual Data Processing & Representation
Module description:
xxx
Imaging Beyond Consumer Cameras (PLUS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students are able to understand the varying acquisition techniques as discussed in the lecture and have knowledge about fundamental algorithms and procedures in the respective areas.
Superior module:
Visual Data Processing & Representation
Module description:
xxx
Imaging Beyond Consumer Cameras (PLUS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge acquired in the lecture in focused projects, program-ming own code related to the tasks defined in the programming projects.
Superior module:
Visual Data Processing & Representation
Module description:
xxx
Mathematics & Modelling (FHS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AISM1MAMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 4 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Students can apply functions in several variables to model problems. They are able to analyze the change behavior of these functions and to determine critical points. They can approximate complex functions by multidimensional polynomials (especially with tangent planes and second order Taylor polynomials). They are able to use gradient based methods to find local minima. They understand selected problems of convex optimization and can solve them with mathematical software. Students are able to calculate the most important matrix decompositions and apply eigenvalue theory to perform the principal components analysis for data. Students can solve multidimensional integrals. They understand the notion of a vector space (VS) with inner product and relate to it in different application areas. They master the coordinate transformation for the change of basis in finite dimensional VSs and are familiar with the relationship to Fourier analysis. They know selected application areas of the mentioned methods.
Superior module:
Mathematics & Modelling
Module description:
xxx
Natural Computation (PLUS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
Upon completion of the course, students are familiar with the fundamental concepts of Natural Computation and understand theoretical foundations as well as application potential.
Superior module:
Data Science & Analytics
Module description:
xxx
Natural Computation (PLUS)
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge acquired in the lecture in focused projects, program-ming own code related to the tasks defined in the programming projects.
Superior module:
Data Science & Analytics
Module description:
xxx
Applied Statistics (FHS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AISM2APSIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Learning Outcomes:
Students can apply methods of inferential statistics to data and communicate the results obtained both verbally and graphically. They can describe data with models and are able to represent dependencies of random variables with graphical models. They know statistical standards and are able to plan, conduct and document experiments. They know applications of random number generators in the area of generative models and can produce corresponding data with mathematical software.
Superior module:
Mathematics & Modelling
Module description:
xxx
Audio Processing (PLUS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
Upon completion of the course, students understand basic principles of audio processing
Superior module:
Audio and Media
Module description:
xxx
Audio Processing (PLUS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
Upon course completion, students are able to design and implement audio effects and subsys-tems which meet sound quality, computational performance, and real-time requirements, and em-bed them into various applications and platforms.
Superior module:
Audio and Media
Module description:
xxx
Digital Signal Processing 2 (FHS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AISM2DSPIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Students know details in digital filter design such as advantages and disadvantages of different filter types and design methods. They understand the problem of quantization of filter coefficients and how to design 2nd order sections IIR filters. They know how to design special filters like notch, comb or median filters and are able to implement them in a low-level programming language (e.g. C). Students understand the concept of adaptive signal processing and can implement an adap-tive LMS filter e.g. for noise cancellation. In general, they can solve complex signal processing problems on a given hardware platform. Students understand the problems of numerical program-ming. They know common number formats and understand details of fixed point and floating-point arithmetic. They understand the principle of applying standard DSP algorithms also for 2D-signals (images).
Superior module:
Digital Signal Processing
Module description:
xxx
Fourier Analysis, Filter Banks & Wavelets (PLUS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students are able to understand the theoretical basics of Fourier transform, filterbanks and wavelets. They are familiar with the mathematical methods of fil-terbanks with perfect reconstruction. They know the explicit formula of the Daubechies filters and wavelets and can apply these filters to digital signals and images. Furthermore, the students un-derstand the mathematical basics of the theory of wavelets and the construction of compactly supported orthogonal wavelets from quadrature mirror filters.
Superior module:
Mathematics & Modelling
Module description:
xxx
Fourier Analysis, Filter Banks & Wavelets (PLUS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students are able to apply techniques from Fourier and Wavelet theory to the analysis of signals with varying dimensionality, both in terms computer programs as well as in terms of theoretical considerations.
Superior module:
Mathematics & Modelling
Module description:
xxx
Machine Learning (FHS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AISM2MLGIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Students understand the assumptions and restrictions implied by a specific model choice in view of statistical learning theory setup and the "no free lunch" theorem. They select from a collection of well-known and widely available ML algorithms, accordingly, parameterize models and assess the impact of different design choices on the network complexity of neural networks. Students detect overfitting and underfitting during the training process and take corresponding counter measures such as regularization. They apply the machine learning models to different types of data (text, images, numerical) for tasks such as classification, representation learning and object detection and thereby construct examples of AI (artificial intelligence) systems.
Superior module:
Data Science & Analytics
Module description:
xxx
Media Data Formats (PLUS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students are able to understand basic principles of compression techniques for image and video data and know the major formats developed for these data types. In particular, they should be aware of the respective advantages and disadvantages of the re-spective formats and should be able to identify suited formats for a given target application taking constraints into consideration.
Superior module:
Audio and Media
Module description:
xxx
Media Data Formats (PLUS)
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
On completion of the course, students are able to use compression libraries and integrate those into a larger application context. Students are aware of lossy compression artefacts and the po-tential impact on applications using data compressed in that manner.
Superior module:
Audio and Media
Module description:
xxx
Agile Project Management (FHS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AISM3APMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Students can apply theoretical and practical project management and software engineering skills in a team, based on the practical implementation of a continuous software engineering project.
Superior module:
Applied Sciences and Methods
Module description:
xxx
Computer Vision (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students understand the theoretical concepts of deep learning in computer vision and are aware of the potential application areas.
Superior module:
Visual Computing
Module description:
xxx
Computer Vision (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Upon completion of the course, students are able to solve various computer vision tasks using PyTorch.
Superior module:
Visual Computing
Module description:
xxx
Ethics & Sustainability (FHS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AISM3ESAIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 1 |
| Examination character | immanent |
Learning Outcomes:
After successfully completing the symposium, students are able to analyse and reflect on ethical-moral dilemmas; to evaluate opinions from a lecture in their own context of action; to argue social issues with a view to their own professional environment; to articulate and justify their own opinion in the group discussion.
Superior module:
Applied Sciences and Methods
Module description:
xxx
Geometric Modelling (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
On completion of the course, students have acquired an in-depth understanding of basic (math-ematical) concepts used in the modeling of curves, surfaces and shapes. They have seen and used basics of differential geometry, and have been exposed to basic topological concepts of curves and surfaces. Both continuous (e.g., spline-based) and discrete (e.g., triangle-based) rep-resentations have been examined.
Superior module:
Visual Computing
Module description:
xxx
Geometric Modelling (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
On completion of the course, students are able to use fundamental concepts of geometric mod-elling in practical applications, ranging from simple exemplary software implementations to the completion of formal proofs and theoretical considerations.
Superior module:
Visual Computing
Module description:
xxx
Master Seminar 1 (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | SE |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Students know the publication lifecycle including the review process. Furthermore, they are able to assess textual, formal and structural quality aspects of scientific papers and scientific presen-tations. Students have hands-on experience with various tools supporting scientific work, includ-ing LateX and Mathematica.
Superior module:
Applied Sciences and Methods
Module description:
xxx
C: Applied Reinforcement Learning (FHS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AISM3ARLIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students identify problems for model-base and model-free reinforcement learning, apply suitable algorithms and assemble solutions using toolboxes. They know how to use real-life simulations by physics engines for Reinforcement Learning and know the challenges when switching to robots or other hardware. They discuss current trends and upcoming areas of application.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Advanced Machine Learning (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
Students will learn a formal-mathematical understanding of this idea. They are exposed to funda-mental concepts such as probably approximately correct (PAC) learning, Vapnik¿Chervonenkis theory and applications thereof. Further, the theoretical understanding of the learning process is applied in the analysis of popular learning algorithms such as Boosting or support vector ma-chines (SVMs) which have become so ubiquitous in many fields of science.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Advanced Machine Learning (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge gained in the lecture and in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application-oriented fields.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Applied Natural Language Processing (FHS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AISM3ANLIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are aware of the difference between task-oriented systems and dialog systems. They develop algorithms for generating natural language targeted at different tasks (slot filling, question answering) or for conversational purposes. Students know about existing tools for the development of dialog systems, their differences and how to integrate these tools into other applications such as social media.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Biometric Systems (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
Students know about the most important biometric traits, their advantages and shortcomings, and the weaknesses and strengths of biometric systems in general.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Biometric Systems (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge gained in the lecture and in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application-oriented fields.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Computational Geometry (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
The students are able to analyze geometric problems and to design algorithms for solving them in an efficient manner. They have been exposed to important paradigms of geometric computing, and have acquired in-depth knowledge of basic geometric data structures (such as triangulations and Voronoi diagrams). They have also seen sample applications of these data structures and algorithms for solving real-world problems of a geometric flavor.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Computational Geometry (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge gained in the lecture and in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application-oriented fields.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Media Security (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
Students know about the most important mechanisms in media security, their advantages and shortcomings, and the weaknesses and strengths of media security systems in general.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Media Security (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge gained in the lecture and in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application-oriented fields.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Medical Imaging (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | VO |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
On completion of the course students are able to understand basics of different medical imaging modalities and their application in a clinical environment.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Medical Imaging (PLUS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | PLUS |
| Type | PS |
| Kind | Elective |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
Students are able to apply their knowledge gained in the lecture and in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application-oriented fields.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Natural Language Processing (FHS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AISM3NLPIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Students apply attention-based models for natural language processing and implement appropriate networks for applications in areas such as machine translation and sentiment analysis in social networks. Building on previously acquired skills in pre-processing text data, they use contextualized text representations and complex network architectures. They are able to decide on network parameters and design appropriate for the problem at hand and know the limits and areas of application of the respective algorithms.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
EC: Reinforcement Learning (FHS)
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AISM3RILIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Students identify problems suited for reinforcement learning, find suitable models and assemble solutions using toolboxes. They distinguish and differentiate between different setups based on input data type and assumptions on the environment and select corresponding algorithms and metrics. Using Deep Learning methodologies, the students design, optimize and evaluate deep reinforcement learning for a set of classical problems. They discuss current trends and upcoming areas of application.
Superior module:
Selected Topics in Applied Image and Signal Processing
Module description:
xxx
Master Exam (PLUS und FHS)
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AISM4MAEDP |
| Type | DP |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 0 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
The students are able to present and discursively defend the hypotheses and solution approaches developed in the master thesis. They are able to establish cross-references to contents of the study program.
Superior module:
Master Thesis & Master Exam
Module description:
xxx
Master Seminar 2 (PLUS oder FHS)
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AISM4MASSE |
| Type | SE |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Learning Outcomes:
The students are able to present and discuss their own scientific work in a peer group situation. They can argue logically and in line with scientific standards as well as understand the importance of a methodical approach.
Superior module:
Master Thesis & Master Exam
Module description:
xxx
Master Thesis (PLUS oder FHS)
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AISM4MATIT |
| Type | IT |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 0 |
| ECTS Credits | 23 |
| Examination character | immanent |
Learning Outcomes:
The students are able to independently write sound academic papers based on common international standards. They can proceed methodically and systematically. They can analyse and present problems, provide solutions as well as formulate these appropriately and critically scrutinise them. The students are able to defend their approach.
Superior module:
Master Thesis & Master Exam
Module description:
xxx
| Legend | |
| Semester | Semesters 1, 3, 5: courses held only in winter semester (mid-September to end of January), Semesters 2, 4, 6: courses held only in summer semester (mid-February to end of June) |
| SWS | weekly contact hours over 14 weeks in semester (example SWS 2 equals 28 contact hours for the whole course |
| ECTS Credits | Work load in ECTS credits, 1 ECTS credit equals an estimated 25 hours of work for the student |
| Type | BP = Bachelor final exam DP/MP = Master final exam IL = Lecture with integrated project work IT = Individual training/phases LB = Lab (session) PS = Pro-seminar PT = Project RC = Course with integrated reflective practice RE = Revision course SE = Seminar TU = Tutorial UB = Practice session/Subject practical sessions VO = Lecture |