Neural Networks and Learning Theory - Sommersemester 2019
| Lecture | Tue | 11:30 - 13:00 | HG 0.19 | Prof. Dr. Klaus Meer |
| Lecture | We | 09:15 - 10:45 | HG 2.44 | Prof. Dr. Klaus Meer |
| Exercise | Fr | 11:30 - 13:00 | HG 0.17 | M. Sc. Ameen Naif |
Course information
- Enrollment:
1. electronically for the module
2. You have to register yourself in Moodle using your BTU account.
There is no password required, registration can be done immediately. - first lecture: 2 April 2019. Here, we shall also discuss potential changes of the lecturing hours.
- first exercise: 5 April 2019
- Final exam: The final exam will an oral one. More details will be given during the course.
Additional Literature
- E. Alpaydin: Maschinelles Lernen, Oldenbourg Verlag München, 2008
- 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, 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, Springer 1996
- S. Shalev-Shwartz, S. Ben-David: Understanding Machine Learning, Cambridge University Press 2014.
Online-Sammlung zahlreicher Artikel:PASCAL 2
Zeitschriften (Auswahl):
- Machine Learning, Springer
- Neural Networks, INNS, Elsevier
- Transactions on Neural Networks, IEEE