Pattern Recognition
Prof. Rolf-Rainer Grigat

Main Study, 2 SWS Lecture, Summer Semester, Implementation in English


Participating students can download the learning material and exercises from Stud.IP.
There is also a forum on the Stud.IP platform.


The lecture treats fundamental methods of the pattern recognition.

Sample applications are the recognition of material defects, contents-based accesses to data bases, the analysis of machine writing and handwriting or the analysis of lip language


  • Basics of the Pattern Recognition
  • MAP Classification, Bayes Classificator, Maximum Likelihood
  • Polynomial Classification (Reduction of the Dimension, Confidence Illustration, Recursive Training)
  • Multilevel Perceptrons (Training by Means of Gradient Descent Procedures)
  • Radially basis Functions, Cluster Analysis