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Methods and Applications of Artificial Intelligence for Signal and Image Processing

Module name (EN):
Name of module in study programme. It should be precise and clear.
Methods and Applications of Artificial Intelligence for Signal and Image Processing
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Electrical Engineering and Information Technology, Bachelor, ASPO 01.10.2018
Module code: E2542
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P211-0291
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4PA (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
8
Semester: 5
Mandatory course: no
Language of instruction:
German
Assessment:
Term paper (25%), seminar presentation (75%)

[updated 08.01.2020]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

E2542 (P211-0291) Electrical Engineering and Information Technology, Bachelor, ASPO 01.10.2018 , semester 5, optional course, technical
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 240 hours (equivalent to 8 ECTS credits).
There are therefore 195 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr.-Ing. Ahmad Osman
Lecturer: Prof. Dr.-Ing. Ahmad Osman

[updated 10.09.2018]
Learning outcomes:
After successfully completing this course, students will have learned and be able to apply the practical and scientific methods of project work to the creation of a term paper based on examples, typical problems and applications from the field of signal and image processing with AI, for example research on the state of the art in image processing, classification methods, regression methods, data compression, data reconstruction, human-machine interaction, literature research (also English technical literature), presentation of project results. Students will be expected to document and explain their approach. They must justify and present their results on the basis of engineering research and and knowledge. Their subsequent presentation must succinctly explain/summarize these aspects and illustrate the use of methods for their project work.

[updated 08.01.2020]
Module content:
Image processing: filtering techniques Image segmentation: region-based or contour-based techniques Classification methods: neural networks, support vector machine etc. Data fusion: the Dempster-Shafer Theory Data reconstruction Data visualization Data compression Human-machine interaction Research to deepen technical or scientific aspects in the form of a supervised term paper. Literature research (also in English) Scientific presentations

[updated 08.01.2020]
Teaching methods/Media:
Term paper with academic supervision in a defined area of specialization or topic using the methods of scientific project work. Participants must be familiar with the state-of-the-art of research/technology in selected areas of AI and be able to critically examine research projects.

[updated 08.01.2020]
Recommended or required reading:
Luger, George F.: Artificial Intelligence, Addison-Wesley, 2009, ISBN 978-0-13-209001-8 Mitchell, Tom M.: Machine learning, McGraw-Hill, 1997, ISBN 978-0-07-042807-2 Russell, Stuart J.; Norvig, Peter: Artificial intelligence: a modern approach, Pearson, 2009, 3rd Ed., ISBN 978-0-13-207148-2

[updated 08.01.2020]
 
[Fri Dec 27 18:48:45 CET 2024, CKEY=e3E2542, BKEY=ei, CID=E2542, LANGUAGE=en, DATE=27.12.2024]