Attention:
Lecture start: 15.04.2026
| Lecture | Wednesday | 11:30 - 13:00 | ZHG, HS B | Prof. Dr. Klaus Meer | ||
| Lecture | Thursday | 09:15 - 10:45 | ZHG, HS B | Prof. Dr. Klaus Meer | ||
| First lecture: 15.April 2026 | ||||||
| Exercise | Friday | 11:30 - 13:00 | ZHG, HS C | M. Sc. Adrian Wurm | ||
| First exercise: 17.April 2026 | ||||||
| Written Exam (90 minutes): 14.09.2026, 11:00 - 13:00, Room: LG 1A, HS 1 | ||||||
important information concerning registration
The module will have a restriction concerning the number of participants.
You therefore have to register online for the module in the period April, 1th 2026 - April 10th 2026.
The further procedure then is as follows: We decide as soon as possible who will be admitted.
You must therefore register for the module online between April, 1th and April 10th, 2026. moodle
| Module (online) registration | Moodle |
| = registration for the exam | = BTU learning platform You have to register once on the platform. Then, at the beginning of the semester you should be enrolled on this platform for courses, in which this platform is used to supoort the study. Either you get a link for the course and you have to enroll yourself or you will be enrolled by the teaching staff. |
| https://www.b-tu.de/qispos11/rds?state=user&type=0&topitem=&breadCrumbSource=&topitem=functions |
Though there are no formal prerequisites for the module, I would like to stress that this will be a module which is focussing on the mathematical aspects of neural networks and learning. This means that you should have at least a well-grounded knowledge on basic mathematics as being typical for an undergraduate program in Computer Science. The latter includes proficiency in basic calculus (such as multivariate differential calculus, optimality conditions etc.), linear algebra (algebraic description of geometric objects like hyperplanes, distances, solving linear systems etc.), and basic probability theory. Necessary results will be recalled briefly, but not to the extent that you can learn it for the first time. If you have no such basic knowledge, the module likely will not be a suitable one for you.
Additional Literature
- E. Alpaydin: Maschinelles Lernen (in German),Oldenbourg Verlag München, 2008
- C.C. Aggarwal: Neural Networks and Deep Learning, Springer, 2018.
- M. Anthony, N.Biggs: Computational Learning Theory, Cambridge University Press 1997
- N. Christiani, J. Shawe-Taylor: An Introduction to Support Vector Machines and kernel-based Learning Methods, Cambridge Univ. Press, 2003
- A.C.C Coolen, R. Kühn, P. Sollich: Theory of Neural Information Processing Systems, Oxford University Press 2005
- P. Fischer: Algorithmisches Lernen (in German), Teubner 1999
- P. Flach: Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press 2012
- F. M. Ham, I. Kostanic: Principles of Neurocomputing for Science & Engineering, McGraw Hill 2001
- S. Haykin: Neural Networks, Prentice Hall, 1999
- R. Rojas: Theorie der neuronalen Netze (in German), Springer 1996
- S. Shalev-Shwartz, S. Ben-David: Understanding Machine Learning, Cambridge University Press 2014.
Some journals/conferences (thought as appetizer):
- Machine Learning, Springer
- Neural Networks, INNS, Elsevier
- Transactions on Neural Networks, IEEE
- Association for Computational L
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