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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:
Wednesday, 8:15 - 9:45; Room: H16
Thursday, 18:15 - 19:45; Room: H16


First lecture: 26.04.2017

First exercises: 02/04.05.2017

First programming exercises: 10.05.2017

StudOn course: Opens external link in new window


Lecture WednesdayLecture ThursdayTheory ExerciseProgramming Exercise
April 26April 27--
May 3May 4May 2, May 4-
May 10May 11-May 10
May 17May 18May 16, May 18-
May 24- (Ascension Day)-May 24
May 31- (Anstich Berg)May 30, June 1-
June 7- (Berg)-June 7
June 14- (Corpus Christi)--
June 21June 22June 20, June 22June 21
June 28! cancelled ! (TF summer party)
June 27, June 29-
July 5July 6-July 5
July 12July 13July 11, July 13-
July 19--July 19
July 26-July 25, July 27-


Please work through the provided literature for each topic:


April 26, 2017Parzen windowsDuda, Hart, Stork: "Pattern Classification", Wiley 2001 (2nd ed.), Section 4.3
May 3, 2017Mean shiftComaniciu, Meer: "Mean Shift: A Robust Approach Toward Feature Space Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, Mai 2002, pp. 603-619.
Download here from within the university network.
May 4, 2017ClusteringHastie, Tibshirani, Friedman: "The Elements of Statistical Learning", Chapter 14 - please read the whole of Chapter 14 (other parts will be treated later in the lecture).
The book is Opens external link in new windowavailable online (but it is also absolutely worth buying, in case you are looking for an excellent read).
May 10, 2017Dirichlet Process ClusteringMurphy: "Machine Learning - A Probabilistic Perspective", MIT Press 2012, Section 25.2.
Prof. Boyd-Graber put a Opens external link in new windowyoutube video online describing the core idea in a nutshell
May 11, 2017Multidimensional ScalingA good description on multidimensional scaling by Herve Abdi can be found online Opens external link in new windowhere.
May 17, 2017IsomapTenenbaum, de Silva, C. Langford: "A Global Geometric Framework for Nonlinear Dimensionality Reduction.", Science vol. 290, no. 5500, Dec. 2000, pp. 2319-2323.
Download Opens external link in new windowhere from within the university network.
May 18, 2017LLERoweis, Saul: "Nonlinear Dimensionality Reduction by Locally Linear Embedding", Science, vol. 290, no. 5500, Dec 2000, pp. 2323-2326.
Download Opens external link in new windowhere from within the university network.
May 24, 2017Laplacian EigenmapsBelkin, Niyogi: "Laplacian Eigenmaps for Dimensionality Reduction and Data Representation", Neural Computation, vol. 15, no. 6, June 2003, pp. 1373-1396.
Download Opens external link in new windowhere from within the university network.
May 31, 2017Random ForestsCriminisi, Shotton, Konukoglu: "Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manyfold Learning and Semi-Supervised Learning", Foundations and Trends in Computer Graphics and Vision, vol. 7, nos. 2-3, 2011, pp. 81-227.
(available online from within the university network)
Criminisi, Shotton: "Decision Forests for Computer Vision and Medical Image Analysis", Springer 2013, Sections 2-7.
(available online from within the university network)
June 21, 2017HMMsBilmes: "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models", U.C. Berkeley, Technical Report TR-97-021, April 1997.
Download Opens external link in new windowhere.
Rabiner: "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition", Proceedings of the IEEE, vol. 77, no. 2, Feb. 1989, pp. 257-286.
Download Opens external link in new windowhere from within the university network.