Module Handbook

  • Dynamischer Default-Fachbereich geändert auf MAT

Course MAT-80-13A-K-4

Introduction to Neural Networks (2V+1U, 4.5 LP)

Course Type

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

Basedata

SWS 2V+1U
CP, Effort 4.5 CP = 135 h
Position of the semester 1 Sem. irreg.
Level [4] Bachelor (Specialization)
Language [EN] English
Lecturers
+ further Lecturers of the department Mathematics
Area of study [MAT-TEMA] Industrial Mathematics
Livecycle-State [NORM] Active

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.

Contents

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.

Literature

  • 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.

Materials

Further literature will be announced in the lecture; Exercise material is provided.

Registration

Registration for the exercise classes via the online administration system URM (https://urm.mathematik.uni-kl.de).

References to Course [MAT-80-13A-K-4]

Module Name Context
[MAT-80-13A-M-4] Introduction to Neural Networks P: Obligatory 2V+1U, 4.5 LP
Course-Pool Name
[MAT-80-2V-KPOOL-4] Elective Courses Modelling and Scientific Computing (2V, B.Sc.)