Modul 11847

Attention:
Lecture start: 17 April 2024

The module will have a restriction concerning the number of participants.
You therefore have to register online for the module in the period 25 March 2024 (2:00 pm)  - 07 April 2024.
The further procedure then is as follows: We decide as soon as possible (April 8,9) who will be admitted.

Update 16 April 2024:
Those who can participate we enrolled in Moodle and informed them via Email.
Students who did not get this information have to cancel their online registration for the module until 20 April 2024

Attention:
Please don`t mix up:  Module registration  -  Moodle

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. 

• E. Alpaydin: Maschinelles Lernen (in German),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 (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 Learning ACL (organizes one of the main conferences on learning theory COLT)