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Pattern Analysis

Lecturers

Details

Time and place:

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

  • Wed 14:15-15:45, Room H16
  • Thu 16:15-17:45, Room H16

Fields of study

  • WPF ME-BA-MG6 from SEM 4
  • PF MT-MA-BDV from SEM 1
  • WPF IuK-MA-MMS-INF from SEM 1
  • WPF ICT-MA-MPS from SEM 1
  • WPF CME-MA from SEM 1
  • WF CME-MA from SEM 1
  • WPF INF-MA from SEM 1
  • WPF CE-MA-INF from SEM 1
  • WF ASC-MA from SEM 1
  • WPF ME-MA-MG6 from SEM 1

Prerequisites / Organizational information

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.

Content

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.

Recommended Literature

- 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 - papers referenced in the lecture

ECTS information

Title

Pattern Analysis

Credits

3,75

Content:

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: 51