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Introduction to Machine Learning (IntroML)- Lecturer
- Dr.-Ing. Vincent Christlein
- Details
- Vorlesung
2 cred.h, certificate, ECTS studies, ECTS credits: 3,75, Sprache Englisch, Information regarding the online teaching will be added to the studon course
Time and place: Wed 8:30 - 10:00, H7
- Fields of study
- WPF ME-BA-MG6 3-5
WPF MT-BA 5
WPF INF-BA-V-ME ab 5
WPF INF-BA-V-MI ab 5
WF CE-BA-TW ab 5
WPF INF-MA 1
WPF IuK-BA ab 5
WPF ME-MA-MG6 1-3
WPF DS-BA ab 3
- Prerequisites / Organisational information
- StudOn: https://www.studon.fau.de/crs4053489.html
- ECTS information:
- Credits: 3,75
- 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. Analog to
digital conversion is briefly discussed with a focus on how it impacts
further signal analysis. 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, Wavelets,
Moments, Principal Component Analysis and Linear Discriminant Analysis. The
lectures conclude with a basic introduction to classification. The
principles of statistical, distribution-free and nonparametric
classification approaches will be presented. Within this context we will
cover Bayesian and Gaussian classifiers. The accompanying exercises will provide further details on the
methods and procedures presented in this lecture with particular emphasis on
their application.
- Literature
- lecture notes
H. Niemann: Klassifikation von Mustern H. Niemann: Pattern Analysis and Understanding S. Theodoridis and K. Koutroumbas: Pattern Recognition, 4th ed., Academic Press, 2009.
- Additional information
- Keywords: Mustererkennung, Vorverarbeitung, Merkmalsextraktion, Klassifikation
Expected participants: 250, Maximale Teilnehmerzahl: 250
- Assigned lectures
- UE ([online]):Introduction to Machine Learning Exercises
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Lecturers: Mathias Seuret, M. Sc., Nora Gourmelon, M. Sc., Mareike Thies, M. Sc.
- UE ([online]):Introduction to Machine Learning Tutorial
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Lecturers: Mathias Seuret, M. Sc., Nora Gourmelon, M. Sc.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2021/2022:
- Daten analysieren und verstehen in den Digital Humanities (DH Analyse)
- Introduction to Machine Learning (IntroML)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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