Course detail

Neural Networks, Adaptive and Optimum Filtering

FIT-QB4Acad. year: 2017/2018

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

Czech

Mode of study

Not applicable.

Learning outcomes of the course unit

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

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Course curriculum

    Work placements

    Not applicable.

    Aims

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

    Specification of controlled education, way of implementation and compensation for absences

    There are no checked study.

    Recommended optional programme components

    Not applicable.

    Prerequisites and corequisites

    Not applicable.

    Basic literature

    Not applicable.

    Recommended reading

    Not applicable.

    Classification of course in study plans

    • Programme CSE-PHD-4 Doctoral

      branch DVI4 , 0 year of study, summer semester, elective