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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Pattern Analysis (PA)- Dozent/in
- Dr.-Ing. Christian Riess
- Angaben
- Vorlesung
3 SWS, benoteter Schein, ECTS-Studium, ECTS-Credits: 5, Sprache Deutsch
Zeit und Ort: Do 14:15 - 15:45, H16; Fr 12:15 - 13:45, H16
- Studienfächer / Studienrichtungen
- WPF MT-MA-BDV 1-4 (ECTS-Credits: 5)
WPF IuK-MA-MMS-INF 1-4 (ECTS-Credits: 5)
WPF IuK-MA-MMS 1-4 (ECTS-Credits: 5)
WPF CME-MA 1-4 (ECTS-Credits: 5)
WF CME-MA 1-4 (ECTS-Credits: 5)
WPF INF-MA 1-4 (ECTS-Credits: 5)
WPF CE-MA-INF ab 1 (ECTS-Credits: 5)
- Voraussetzungen / Organisatorisches
- Pattern Recognition
- Inhalt
- This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition".
In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data.
Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests.
The lecture is accompanied by exercises, where theoretical results are
practically implemented and applied.
- Empfohlene Literatur
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006
Richard O. Duda, Peter E. Hart und David G. Stork, Pattern Classification, Second Edition, 2004
Trevor Hastie, Robert Tibshirani und Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer Verlag, 2009
- ECTS-Informationen:
- Title:
- Pattern Analysis
- Credits: 5
- Prerequisites
- Pattern Recognition
- Contents
- This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". In this third edition, we focus on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests. The lecture is accompanied by exercises, where theoretical results are practically implemented and applied.
- Literature
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006
Richard O. Duda, Peter E. Hart und David G. Stork, Pattern Classification, Second Edition, 2004 Trevor Hastie, Robert Tibshirani und Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer Verlag, 2009
- Zusätzliche Informationen
- Schlagwörter: Mustererkennung, Musteranalyse
Erwartete Teilnehmerzahl: 60, Maximale Teilnehmerzahl: 80
www: http://www5.informatik.uni-erlangen.de/lectures/ss-16/pattern-analysis-pa/
- Zugeordnete Lehrveranstaltungen
- UE: Pattern Analysis Exercises
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Dozent/in: Sebastian Käppler, M. Sc.
www: http://www5.cs.fau.de/lectures/ss-16/pattern-analysis-pa/exercises/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2016:
- Pattern Analysis (lecture + exercises) (PA-VÜ)
- Pattern Analysis (lecture only) (PA-V)
- Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
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