|
|
Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science) >>
|
Pattern Analysis (PA)- Dozent/in
- Dr.-Ing. Christian Riess
- Angaben
- Vorlesung
3 SWS, benoteter Schein, ECTS-Studium, ECTS-Credits: 3,75, Sprache Deutsch
Zeit und Ort: Mi 8:15 - 9:45, H16; Do 18:15 - 19:45, H16
- Studienfächer / Studienrichtungen
- PF 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)
WF ASC-MA 1-4
- 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
- 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-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: 37, Maximale Teilnehmerzahl: 80
www: http://www5.informatik.uni-erlangen.de/lectures/ss-17/pattern-analysis-pa/
- Zugeordnete Lehrveranstaltungen
- UE: Pattern Analysis Exercises
-
Dozent/in: Sebastian Käppler, M. Sc.
www: http://www5.cs.fau.de/lectures/ss-17/pattern-analysis-pa/exercises/
- UE: Pattern Analysis Programming
-
Dozent/in: Sebastian Käppler, M. Sc.
www: http://www5.cs.fau.de/lectures/ss-17/pattern-analysis-pa/exercises/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2017:
- Pattern Analysis (PA)
- Pattern Analysis Deluxe (PA DX)
- Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
|
 |
|
|
UnivIS ist ein Produkt der Config eG, Buckenhof |
|
|