Introduction to Neural Networks (2V+1U, 4.5 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|-||K||Lecture with exercise classes (V/U)|
|2||V||Lecture||3.0 CP||28 h||62 h|
|1||U||Exercise class (in small groups)||1.5 CP||14 h||31 h|
|(2V+1U)||4.5 CP||42 h||93 h|
|CP, Effort||4.5 CP = 135 h|
|Position of the semester||1 Sem. irreg.|
|Level|| Bachelor (Specialization)|
+ further Lecturers of the department Mathematics
|Area of study||[MAT-TEMA] Industrial Mathematics|
Possible Study achievement
- Verification of study performance: proof of successful participation in the exercise classes (ungraded)
- Details of the examination (type, duration, criteria) will be announced at the beginning of the course.
Basic terms and ideas of the theory of neural networks as well as their applications are discussed. In particular, the following contents will be covered:
- easy perceptrons, multi-(hidden-)layer-perceptrons,
- propositions about separation and classification,
- basics of supervised and unsupervised learning.
- S. Haykin: Neural Networks and Learning Machines: A Comprehensive Foundation,
- M.T. Hagan, H.B. Demuth, M. Beale: Neural Network Design,
- M.L. Minsky, S.A. Papert: Perceptrons.
Further literature will be announced in the lecture; Exercise material is provided.
Registration for the exercise classes via the online administration system URM (https://urm.mathematik.uni-kl.de).
Requirements for attendance (informal)
- [MAT-10-1-M-2] Fundamentals of Mathematics (M, 28.0 LP)
- [MAT-14-11-M-3] Introduction to Numerical Methods (M, 9.0 LP)
Requirements for attendance (formal)