Pattern Recognition

Dozent:            
Dr.-Ing. Michael Dorr
Assistent:        Mikhail Startsev, Ioannis Agtzidis
Turnus:SS
Zielgruppe:Wahlmodul zur fachlichen Vertiefung, MSEI, MSNE
ECTS:5 ECTS
Umfang:2 VO / 2 UE
Zeit & Ort:

Lecture:    Wednesday, 13:15 - 14:45, N1189  starting at 26.04.2017
Tutorial:   
Friday,         11:30 - 13:00, 1402  starting at 19.05.2017
and           Thursday,     15:00 - 16:30, 2370  starting at 18.05.2017

Exam SS17

For the exam in Pattern Recognition on 26th July at 1:30 pm the room assignment is as following:

1601, Hörsaal, Theresianum:                 Abel - Das
2300, Friedrich von Thiersch Hörsaal:   Dehmani - Markl
N1189, Hans-Piloty-Hörsaal:                  Merk - Tsai
2770, Hörsaal:                                       Tunuguntla - Zylka

Content

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

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