Neuronale Netze und Lerntheorie

Students will get insight into different network architectures and their principles of operation. Notions like artificial intelligence and automatic learning will be made precise during the course. A central issue is the understanding of mathematical ideas underlying different network learning algorithms. This includes both positive solutions of problems and knowledge about limits of the approaches studied.

• Successful completion of homework (fortnightly) and/or successful completion of tests (approx. 4 tests of 15-30 minutes each, written during the lecture period)
• Written examination, 90 min. OR
• Oral examination, 30-45 min. (with small number of participants)

Die einzigen Termine im WiSe 26/27 sind Prüfungstermine – gelehrt wird das Modul in diesem Semester nicht.

• 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.

• 12450 Neuronale Netze und Lerntheorie

Bewertungen von Studierenden

Was Studierende über dieses Modul sagen. Die BTU hat damit nichts zu tun — hier schreiben nur Studierende, und jede Bewertung ist die Meinung einer einzelnen Person.