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Pattern Analysis [PA]

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.

Dates & Rooms:
Thursday, 12:15 - 13:45; Room: H16
Tuesday, 12:15 - 13:45; Room: H16



  • Exercises are roughly every second week (plus/minus holidays)
  • Please note that successful participation in the exercises is required!
  • Registration to the exercises is organized in studon
  • All lecture materials will be posted in studon
  • Exam appointments will be distributed in the lecture in June (details follow)


Tue. LectureThu. LectureTue. ExerciseWed. Exercise
April 10April 12--
--April 17April 18
April 24April 26April 24April 25
-May 3-May 2
May 8-May 8May 9
CANCELLEDMay 17May 15May 16
-May 24-May 23
May 29-May 29May 30
June 5June 7June 5June 6
June 12June 14June 12June 13
June 19June 21June 19June 20
June 26June 28June 26June 27
July 3(reserve)July 3July 4