Machine Learning in Signal Processing (MLSIP)- Dozentinnen/Dozenten
- Lehrbeauftragte, Christian Hümmer, M. Sc.
- Angaben
- Vorlesung
3 SWS, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch
Zeit und Ort: Blockveranstaltung 7.8.2017-11.8.2017 Mo-Fr 8:15 - 11:30, H15; Bemerkung zu Zeit und Ort: Block course in August 2017
- Studienfächer / Studienrichtungen
- PF ASC-MA 1-4 (ECTS-Credits: 5)
WPF CME-MA 1-4 (ECTS-Credits: 5)
WPF IuK-MA-ES 1-4 (ECTS-Credits: 5)
WPF IuK-MA-MMS 1-4 (ECTS-Credits: 5)
WPF IuK-MA-KOMÜ 1-4 (ECTS-Credits: 5)
WPF CE-MA-TA-IT 1-4 (ECTS-Credits: 5)
- Inhalt
- The goal of this lecture is to familiarize the students with the overall pipeline of a pattern recognition system. The various steps involved from data capture to pattern classification are presented. The lectures start with a short introduction, where the nomenclature is defined. Commonly used preprocessing methods are then described. A key component of pattern recognition is feature extraction. Thus, several techniques for feature computation will be presented including Walsh transform, Haar transform, linear predictive coding (LPC), wavelets, moments, principal component analysis (PCA) and linear discriminant analysis (LDA). The lectures conclude with a basic introduction to classification. The principles of statistical, distribution-free and non-parametric classification approaches will be presented. Within this context we will cover Bayesian and Gaussian classifiers, as well as artificial neural networks.
- ECTS-Informationen:
- Credits: 5
- Contents
- The goal of this lecture is to familiarize the students with the overall pipeline of a pattern recognition system. The various steps involved from data capture to pattern classification are presented. The lectures start with a short introduction, where the nomenclature is defined. Commonly used preprocessing methods are then described. A key component of pattern recognition is feature extraction. Thus, several techniques for feature computation will be presented including Walsh transform, Haar transform, linear predictive coding (LPC), wavelets, moments, principal component analysis (PCA) and linear discriminant analysis (LDA). The lectures conclude with a basic introduction to classification. The principles of statistical, distribution-free and non-parametric classification approaches will be presented. Within this context we will cover Bayesian and Gaussian classifiers, as well as artificial neural networks.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 30
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2017:
- Machine Learning in Signal Processing (MLSIP)
- Institution: Lehrstuhl für Multimediakommunikation und Signalverarbeitung
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