Artificial Intelligence for autonomous systems (KIS)
6/7. Semester
5 ECTS | 4 SWS
Presentation (R)
Make Machines Intelligent: Develop AI systems that act autonomously—for example in image recognition, speech processing, or motion control—and apply them to solve real-world problems.
Contents
- Artificial Intelligence methods
- Planning algorithms
- Evolutionary Algorithms in the simulation
- Machine Learning in real world szenarios
- Neural Networks for image classification, speech recognition, for steering of movements etc.
Competencies
Students are able to
- understand Artificial Intelligence Methods
- understand planning algorithms
- apply Evolutionary Algorithms
- to develop a Machine Learning approaches for the real world
- to apply Neural Networks to different application szenarios
Literature
- Fischer, Jörn: Maschinelles Lernen für Dummies, Wiley CH, ISBN: 978-3527720552, 2024
- S. Russell, P. Norvig, Artificial Intelligence A modern approach, ISBN: 978-0132071482, 2010
- I. Goodfellow, Y.Bengio, A. Courville : Deep Learning, MIT Press, ISBN: 978-0262035613, 2016
- Mitchell, Tom: Machine Learning. McGraw-Hill, 1997
- Zell, Andreas: Simulation Neuronaler Netze. Oldenbourg Verlag, München, 1997
Lecturer
- Prof. Dr. Jörn Fischer
- Prof. Dr. Thomas Ihme
Recommended Previous Knowledge
-
Lineare Algebra, Analysis, MLE von Vorteil
Module Details
| Semester |
6/7 |
| Lecture Language |
German |
|
Frequency
|
Not regularly
|
| Credit Points (ECTS)
|
5 |
| Course Coordinator |
Prof. Dr. Jörn Fischer |
| Duration |
1 Semester |
|
Course Achievement
|
None |
|
Prerequisite for exam
|
Project (PA) |
|
Exam
|
Presentation (R) |
Weekly Hours (SWS)
| Lecture |
2 SWS |
| Project |
2 SWS |
| Sum |
4 SWS |
Work Load
| Lecture |
30 h |
| Self Study |
90 h |
| Project |
30 h |
| Sum |
150 h |
