11847 - Neural Networks and Learning Theory Modulübersicht

Module Number: 11847
Module Title:Neural Networks and Learning Theory
  Neuronale Netze und Lerntheorie
Department: Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
Responsible Staff Member:
  • Prof. Dr. rer. nat. habil Meer, Klaus
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Each summer semester even year
Credits: 8
Learning Outcome: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.
Contents:Some central network architectures are treated. These architectures differ in the way they manipulate input data, the way they perform learning tasks and the analysis of corresponding algorithms by mathematical means. More precisely, the following types of networks are covered:
  • General aspects of architecures, in particular feedforward nets, recurrent nets
  • Perceptron network, perceptron learning algorithm
  • Backpropagation algorithm
  • Radial basis function networks
  • Support Vector Machines
  • Learning theory and Vapnik-Chervonenkis dimension
  • Self-organizing networks
  • Hopfield networks
Special emphasis will be given to the mathematical analysis of algorithms. This will make it necessary to study some basic facts of optimization and probability theory.
Recommended Prerequisites:Basic knowledge both concerning optimality criteria in differentiable optimization and probability theory are advisable, but will be treated briefly in the course.
Solid knowledge of the content of module
  • 11213: Mathematik IT -3 (Analysis)
Mandatory Prerequisites:No successful participation in associated phase-out module 12450 Neuronale Netze und Lerntheorie.
Forms of Teaching and Proportion:
  • Lecture / 4 Hours per Week per Semester
  • Exercise / 2 Hours per Week per Semester
  • Self organised studies / 150 Hours
Teaching Materials and 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.
Module Examination:Final Module Examination (MAP)
Assessment Mode for Module Examination:
  • Written examination, 90 min. OR
  • Oral examination, 30-45 min. (with small number of participants)
In the first lecture it will be announced, if the examination will be offered in written or oral form.
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:100
Part of the Study Programme:
  • Master (research-oriented) / Angewandte Mathematik / PO 2008
  • Master (research-oriented) / Angewandte Mathematik / PO 2019
  • Abschluss im Ausland / Artificial Intelligence / keine PO
  • Master (research-oriented) / Artificial Intelligence / PO 2022
  • Abschluss im Ausland / Cyber Security / keine PO
  • Master (research-oriented) / Cyber Security / PO 2017
  • Master (research-oriented) / Informatik / PO 2008
  • Master (research-oriented) / Informations- und Medientechnik / PO 2017
  • Master (research-oriented) / Künstliche Intelligenz Technologie / PO 2022
  • Master (research-oriented) / Maschinenbau / PO 2023
  • Master (research-oriented) - Reduced Semester / Maschinenbau / PO 2023
  • Master (research-oriented) - Co-Op Programme with Practical Place / Maschinenbau - dual / PO 2023
  • Abschluss im Ausland / Mathematik / keine PO
  • Bachelor (research-oriented) / Mathematik / PO 2023
  • Bachelor (research-oriented) - Co-Op Programme with Practical Placement / Mathematik - dual / PO 2023
  • Master (research-oriented) / Micro- and Nanoelectronics / PO 2024
  • Master (research-oriented) / Physics / PO 2021
  • Bachelor (research-oriented) / Wirtschaftsmathematik / PO 2023
  • Bachelor (research-oriented) - Co-Op Programme with Practical Placement / Wirtschaftsmathematik - dual / PO 2023
  • Study programme Informatik M.Sc.: Compulsory elective module in complex „Grundlagen der Informatik" (level 400)
  • Study programme Cyber Security M.Sc.: Compulsory elective module in complex „Computer Science"
  • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Learning and Reasoning“
  • Study programme Künstliche Intelligenz Technologie M.Sc.: Compulsory elective module in complex „Kognitions- und Neurowissenschaft“
  • Study programme Angewandte Mathematik M.Sc.: Compulsory elective module in complex „Analysis / Algebra / Kombinatorik“
  • Study programme Mathematik B.Sc.: Compulsory elective module in complex „Vertiefung“, in limited extend
  • Study programme Wirtschaftsmathematik B.Sc.: Compulsory elective module in complex „Vertiefung“, in limited extend
  • Study programme Physics M.Sc.: Compulsory elective module in complex „Minor Subject“
Module Components:
  • Lecture: Neural Networks and Learning Theory
  • Accompanying exercise
  • Related examination
Components to be offered in the Current Semester:
Phase-out Module: Follow-up Module since: 06.10.2017