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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>

  Pattern Recognition (PR)

Dozent/in
Prof. Dr.-Ing. Elmar Nöth

Angaben
Vorlesung
3 SWS, Schein, ECTS-Studium, ECTS-Credits: 3,75
geeignet als Schlüsselqualifikation, Sprache Englisch
Zeit und Ort: Mo 10:15 - 11:45, H4; Di 14:15 - 15:45, C2 - Chemikum
ab 15.10.2019

Studienfächer / Studienrichtungen
WPF MT-MA-BDV 1-3
PF IuK-MA-MMS-INF ab 1
PF ICT-MA-MPS 1-4
WPF CE-MA-INF ab 1
WPF INF-MA ab 1
WPF CME-MA ab 1
WF ASC-MA 1-4

Voraussetzungen / Organisatorisches
Please note:
All participants should register in StudOn under "Pattern Recognition WS2019/20".
The first class on Oct. 14, 2019, will not take place due to introductory events for the first semester Bachelor students.

ECTS-Informationen:
Title:
Pattern Recognition

Credits: 3,75

Contents
This lecture gives an introduction into the basic and commonly used classification concepts. First the necessary statistical concepts are revised and the Bayes classifier is introduced. Further concepts include generative and discriminative models such as the Gaussian classifier and Naive Bayes, and logistic regression, Linear Discriminant Analysis, the Perceptron and Support Vector Machines (SVMs). Finally more complex methods like the Expectation Maximization Algorithm, which is used to estimate the parameters of Gaussian Mixture Models (GMM), are discussed.
In addition to the mentioned classifiers, methods necessary for practical application like dimensionality reduction, optimization methods and the use of kernel functions are explained.
Finally, we focus on Independent Component Analysis (ICA), combine weak classifiers to get a strong one (AdaBoost), and discuss the performance of machine classifiers.
In the tutorials the methods and procedures that are presented in this lecture are illustrated using theoretical and practical exercises.

Literature
  • lecture notes
  • Duda R., Hart P. and Stork D.: Pattern Classification

  • Niemann H.: Klassifikation von Mustern

  • Niemann H.: Pattern Analysis and Understanding

Zusätzliche Informationen
Schlagwörter: Mustererkennung, maschinelle Klassifikation
Erwartete Teilnehmerzahl: 26, Maximale Teilnehmerzahl: 150
www: http://www5.cs.fau.de/lectures/ws-1920/pattern-recognition-pr/

Zugeordnete Lehrveranstaltungen
UE: Pattern Recognition Exercises
Dozentinnen/Dozenten: Stephan Seitz, M. Sc., Dalia Rodriguez Salas, M.Eng.
www: http://www5.cs.fau.de/lectures/ws-1920/pattern-recognition-pr/
UE: Pattern Recognition Programming
Dozentinnen/Dozenten: Lina Felsner, M. Sc., Dalia Rodriguez Salas, M.Eng.
www: http://www5.cs.fau.de/lectures/ws-1920/pattern-recognition-pr/

Verwendung in folgenden UnivIS-Modulen
Startsemester WS 2019/2020:
Pattern Recognition (PR)
Pattern Recognition Deluxe (PR)

Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
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