- easy perceptrons, multi-(hidden-)layer-perceptrons,
- propositions about separation and classification,
- basics of supervised and unsupervised learning.
Introduction to Neural Networks (M, 4.5 LP)
|Module Number||Module Name||CP (Effort)|
|MAT-80-13A-M-4||Introduction to Neural Networks||4.5 CP (135 h)|
|CP, Effort||4.5 CP = 135 h|
|Position of the semester||1 Sem. irreg.|
|Level|| Bachelor (Specialization)|
|Area of study||[MAT-TEMA] Industrial Mathematics|
|Reference course of study||[MAT-88.105-SG] M.Sc. Mathematics|
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Introduction to Neural Networks
|P||42 h||93 h||
- About [MAT-80-13A-K-4]: Title: "Introduction to Neural Networks"; Presence-Time: 42 h; Self-Study: 93 h
- About [MAT-80-13A-K-4]: The study achievement [U-Schein] proof of successful participation in the exercise classes (ungraded) must be obtained.
Examination achievement PL1
- Form of examination: oral examination (20-30 Min.)
- Examination Frequency: irregular (by arrangement)
- Examination number: 84280 ("Introduction to Neural Networks")
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
By completing the given exercises, the students have developed a skilled, precise and independent handling of the terms, propositions and techniques taught in the lecture. In addition, they have learnt how to apply these techniques to new problems, analyze them and develop solution strategies independently or by team work.
- 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.
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)