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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Pattern Recognition (PR) [Import]- Dozentinnen/Dozenten
- Prof. Dr.-Ing. habil. Andreas Maier, Paul Stöwer, M. Sc.
- Angaben
- Vorlesung
Online
3 SWS, Schein, ECTS-Studium, ECTS-Credits: 3,75
geeignet als Schlüsselqualifikation, Sprache Englisch, This class will be given purely on fau.tv. Short videos will be posted on a regular schedule (not necessary the in-person time mentioned here at UnivIs)
Zeit: Mo 14:15 - 15:45, H4; Do 10:15 - 11:45, H4
- Studienfächer / Studienrichtungen
- WPF ME-BA-MG6 3-5
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
WF CE-BA-TW ab 5
WPF INF-MA ab 1
WPF CME-MA ab 1
WF ASC-MA 1-4
WPF ME-MA-MG6 1-3
WPF DS-MA ab 1
- 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: 150, Maximale Teilnehmerzahl: 500
- Zugeordnete Lehrveranstaltungen
- UE: Pattern Recognition Exercises
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Dozentinnen/Dozenten: Paul Stöwer, M. Sc., Siming Bayer, M. Sc.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2021/2022:
- Pattern Recognition (PR)
- Institution: Geschäftsstelle MAOT
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