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Communications and Multimedia Engineering (Master of Science) >>
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Pattern Analysis (PA)- Lecturer
- Dr.-Ing. Christian Riess
- Details
- Vorlesung
3 cred.h, benoteter certificate, ECTS studies, ECTS credits: 3,75, Sprache Deutsch
Time and place: Mon 16:15 - 17:45, H16; Wed 10:15 - 11:45, H16
- Fields of study
- 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
- Prerequisites / Organisational information
- 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.
- Recommended literature
- Richard O. Duda, Peter E. Hart und David G. Stork: Pattern Classification, Second Edition, 2004
Christopher Bishop: Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006
Antonio Criminisi and J. Shotton: Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013
Kevin P. Murphy: Machine Learning: A Probabilistic Perspective, MIT Press, 2012
papers referenced in the lecture
- ECTS information:
- 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
- Additional information
- Keywords: pattern recognition, pattern analysis
Expected participants: 54, Maximale Teilnehmerzahl: 80
www: http://www5.informatik.uni-erlangen.de/lectures/ss-19/pattern-analysis-pa/
- Assigned lectures
- UE: Pattern Analysis Programming
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Lecturers: Daniel Stromer, M. Sc., Dalia Rodriguez Salas, M.Eng., AmirAbbas Davari, M. Sc.
www: http://www5.cs.fau.de/lectures/ss-19/pattern-analysis-pa/exercises/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2019:
- Pattern Analysis (PA)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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