Artificial Neural Networks

Module Code
BI5001
Module Coordinators
Andreas Peter Dominik
Teachers
Andreas Peter Dominik
Short Description
The module delivers detailed knowledge of the function of artificial neural networks and shows opportunities as well as limitations for typical fields of application.
Learning Objectives

Students are familiar with the concept and algorithms and im-plementations of artificial neural networks. They are capable to apply ANNs and to develop own implementations. They can evaluate and discuss the advantages and disadvantages of neural architectures. Students are able to present and defend their work in front of a public seminar.

Contents
  • Biological background
  • Types of ANNs
  • Application of ANNs to problems in the field of life sciences (bioinformatics)
  • Programming of own tools
Duration in Semester
1
Instruction Language
German
Total Effort
6 CrP; an estimated 180 hours, of which approximately 60 are spent in class.
Weekly School Hours
4
Method of Instruction
Lecture 2 sppw Practical Course 2 sppw
Requirements for the awarding of Credit Points
Written exam
Evaluation Standard
according to examination regulations (§ 9)
Availability
Yearly
References
  • A. Zell: Simulation Neuronale Netze Oldenbourg
  • Johann Gasteiger: Neural Networks in Chemistry and Drug Design Wiley-VCH
  • Mat Buckland: Neural Networks in Plain English http://www.ai-junkie.com/ann/evolved/nnt1.html
Prerequisites
None