Course details

Neural Networks, Adaptive and Optimum Filtering

QB4 Acad. year 2019/2020 Summer semester

Current academic year

In its first part, the course is devoted to providing an overview of types of architecture of neural networks and to a detailed analysis of their properties. Applications of neural networks in signal and image processing and recognition are included in this treatment. In the second part, the course deals with the theory of optimum detection and restoration of signals in its classical and generalised forms, emphasising the common base of this whole area. The subject highlights the common view-points in the area of neural networks and in the area of optimised signal processing.


Language of instruction



Examination (oral)

Time span

39 hrs lectures

Assessment points

100 exam



Subject specific learning outcomes and competences

Theoretical knowledge of areas of neural networks and optimum signal processing, ability to apply and, if necessary, to modify these methods for concrete problems.

Learning objectives

Gaining knowledge of theory of neural networks and theory of adaptive and optimum filtering, showing common view-points of both areas

Prerequisite kwnowledge and skills

signal and system theory, digital signal processing (e.g. the subjects BCZA, MMZS)

Course inclusion in study plans

Back to top