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Advanced Signal Processing & Communications Engineering (Master of Science) >>
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Pattern Analysis (PA)- Dozent/in
- PD Dr.-Ing. Christian Riess
- Angaben
- Vorlesung
Online
3 SWS, benoteter Schein, ECTS-Studium, ECTS-Credits: 3,75, Sprache Englisch, This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again
Zeit: Di, Fr 12:15 - 13:45, H16
- Studienfächer / Studienrichtungen
- WPF ME-BA-MG6 4-6 (ECTS-Credits: 5)
PF MT-MA-BDV 1-4 (ECTS-Credits: 5)
WPF IuK-MA-MMS-INF 1-4 (ECTS-Credits: 5)
WPF ICT-MA-MPS 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)
WF ASC-MA 1-4 (ECTS-Credits: 5)
WPF ME-MA-MG6 1-3 (ECTS-Credits: 5)
WPF AI-MA ab 1 (ECTS-Credits: 5)
- Voraussetzungen / Organisatorisches
- Please join the class "Pattern Analysis" in studOn. All lecture material will be linked and made available there.
It is recommended (but not mandatory) that participants attend the lecture Pattern Recognition first.
- Inhalt
- This lecture complements the lectures "Introduction to Pattern Recognition" and "Pattern Recognition".
In this third edition, we focus on analyzing and simplifying feature representations.
Major topics of this lecture are density estimation, clustering, 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.
To participate, please join the Pattern Analysis studOn class: https://www.studon.fau.de/crs3708405_join.html
- Empfohlene Literatur
- Christopher Bishop: Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006
T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning, 2nd edition, Springer Verlag, 2017.
Antonio Criminisi and J. Shotton: Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013
- ECTS-Informationen:
- Title:
- Pattern Analysis
- Credits: 3,75
- 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: pattern recognition, pattern analysis
Erwartete Teilnehmerzahl: 61, Maximale Teilnehmerzahl: 80
www: https://www.studon.fau.de/crs3708405_join.html
- Zugeordnete Lehrveranstaltungen
- UE ([online]):Pattern Analysis Programming
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Dozentinnen/Dozenten: Mathias Seuret, M. Sc., Zhaoya Pan, M. Sc.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2021:
- Pattern Analysis (PA)
- Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
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