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263-9000-00L 4 Credits BSC , DS , MSC D-ITET , D-INFK , D-PHYS , D-MATH

Information Processing with Neural Networks

Informationsverarbeitung in neuronalen Netzwerken

Lecturers & Examiners: Dr. Jakob Bernasconi
VVZ CR n/a

Last Updated: 2026-02-05 15:29:35

Abstract

Information processing with artificial neural networks(Basic principles and applications)

Objective

The course gives an introduction to the different methods and techniques of information processing with artificial neural networks. Its aim is to provide the necessary background for an efficient use of these new information processing techniques.

Content

Artificial neurons, different types of neural network paradigms (feedforward networks, Hopfield networks, winner-take-all networks), learning procedures (error backpropagation, stochastic learning, reinforcement learning, competitive learning), analysis and optimization of learning and generalization behavior, discussion and analysis of applications.

Resources

Lecture Notes

Course script (including a list of further references).

Literature

Course script (with additional literature references)

General Information

Language
German
Levels
BSC , DS , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Taschenrechner; keine anderen Hilfsmittel

Course Components

Type Title Time & Place Hours
lecture Informationsverarbeitung in neuronalen Netzwerken
  • Fri 10:15-12:00 (IFW A 36)
2 h weekly
exercise Informationsverarbeitung in neuronalen Netzwerken
  • Fri 12:15-13:00 (IFW A 36)
1 h weekly

Offered In