Pattern Recognition

Dr.-Ing. Michael Dorr
Assistent:        Mikhail Startsev, Ioannis Agtzidis
Zielgruppe:Wahlmodul zur fachlichen Vertiefung, MSEI, MSNE
Umfang:2 VO / 2 UE
Zeit & Ort:

Lecture:    Wednesday, 13:15 - 14:45, N1189  starting at 11.04.2018
Thursday, 15:00 - 16:30, 2370 starting at 26.04.2018
                  Friday, 11:30 - 13:00, 1402  starting at 27.04.2018         

Exam in SS18

The exam in Pattern Recognition takes place on 1st August at 1:30 pm.

Room assignment:

0980, Audimax:  Abichou - Liu
                          -,Syed Sha Qutub
                          -,Vinod Ramachandra

1986, Audimax Galerie: Lober - Thabet

N1190, Hans-Heinrich-Meinke-Hörsaal: Thoma - Zou


Pattern recognition applications, feature extraction for patterns, data preprocessing, distance classifiers, decision functions, polynomial classifiers, clustering methods, self-organizing maps, Bayes classifiers, Maximum Likelihood methods, probabilistic inference, VC dimension, decision trees and random forests, perceptron, support vector machines.

Course Outline

From unlocking your phone with your fingerprint to speech-controlled 'personal assistants', to automated diagnosis of life-threatening diseases and to highly autonomous driving - Pattern Recognition is everywhere. While many of the recent breakthroughs in Pattern Recognition applications have been enabled by leaps in computational power and very large data sets, the fundamental algorithms and concepts are actually quite simple and mostly have been around for decades. In this lecture with its accompanying exercise, we will cover the following topics:

  • Pattern recognition applications
  • feature extraction for patterns
  • data preprocessing
  • distance classifiers
  • decision functions
  • polynomial classifiers
  • clustering methods
  • self-organizing maps
  • Bayes classifiers
  • Maximum Likelihood methods
  • probabilistic inference
  • VC dimension
  • decision trees and random forests
  • perceptron
  • support vector machines

Complementary Books

The following literature is recommended:

  • R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2.Auflage, John Wiley & Sons, 2001.
  • C. Bishop, Pattern Recognition and Machine Learning, Springer, 2007