• Course Info
• Lectures
• Assignments
• Project
• Grading
• Links

Announcements

1. (Jan 29) Syllabus is available.
2. (Jan 30) Slides for Introduction to Pattern Recognition are available.
3. (Feb 4) Slides for Bayesian Decision Theory are available.
4. (Feb 8) Lecture schedule is updated.
5. (Feb 14) First part of the slides for Parametric Models is available.
6. (Feb 22) Second part of the slides for Parametric Models is available.
7. (Feb 22) Office hours are updated.
8. (Feb 23) Homework assignment 1 is available.
9. (Mar 5) Third part of the slides for Parametric Models is available.
10. (Mar 13) Fourth part of the slides for Parametric Models is available.
11. (Mar 19) Slides for Non-parametric Methods are available.
12. (Mar 21) Updated slides for Hidden Markov Models.
13. (Mar 21) Homework assignment 2 is available.
14. (Mar 22) Slides for Feature Reduction and Selection are available.
15. (Apr 4) First part of the slides for Non-Bayesian Classifiers is available.
16. (Apr 10) Second part of the slides for Non-Bayesian Classifiers is available.
17. (Apr 10) There will not be any class during the week of April 17.
18. (Apr 11) Third part of the slides for Non-Bayesian Classifiers is available.
19. (Apr 23) Slides for Unsupervised Learning and Clustering are available.
20. (Apr 26) Homework assignment 3 is available.
21. (May 1) Due date for project progress reports is postponed to May 8th, and due date for homework 3 is postponed to May 12th.
22. (May 2) Grades for the first two quizzes are available.
23. (May 2) Slides for Algorithm-Independent Learning Issues are available.
24. (May 9) Slides for Structural and Syntactic Pattern Recognition are available.
25. (May 14) Updated slides for Structural and Syntactic Pattern Recognition with examples for pattern description using grammars.
26. (May 18) Grades for the first homework assignment are available.
27. (May 21) Project final report due date and presentation date are postponed by one day.
28. (May 24) Grades for the third quiz are available.
29. (May 24) Poster presentations will be made at the entrance floor of the EB building (in front of the Mithat Coruh auditorium) during 14:30-17:00 on May 25th.
30. (May 24) You should submit your project software and data using the online form by the end of May 25th.
31. (May 30) Grades for the second and third homework assignments are available.
32. Added all projects' final reports.

Personnel

Instructor:	Selim Aksoy
Office:	EA 423
Email:
Office Hours:	Wed 13:40-15:30

Course Information

Schedule:	Mon 9:00-10:20, Wed 9:40-11:00 (EA 502)
Mailing List:	http://retina.cs.bilkent.edu.tr/mailman/listinfo/cs551-spring2006
Prerequisites:	Probability theory, statistics, linear algebra
Texts:	R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000. (required) A. Webb, Statistical Pattern Recognition, Arnold Publishers, 1999. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 1990. R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches, John Wiley & Sons, Inc., 1992. A. K. Jain, R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.

Lecture Schedule

Chapters	Contents
Introduction to Pattern Recognition [ Slides ] (Feb 1, 6)	Topics: Pattern recognition systems The design cycle An example Readings: DHS Ch 1, Appendix A.1-A.2, A.4-A.5 References: A. K. Jain, R. P. W. Duin, J. Mao, "Statistical Pattern Recognition: A Review" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(1):4-37, January 2000.
Bayesian Decision Theory [ Slides ] (Feb 8, 13, 15)	Topics: Modeling using continuous and discrete features Discriminant functions The Gaussian density Error estimation Readings: DHS Ch 2.1-2.9
Parametric Models [ Slides: Part 1 | Part 2 | Part 3 | Part 4 ] (Feb 20, 22, 27, Mar 6, 8, 13, 15)	Topics: Maximum-likelihood estimation Bayesian estimation Expectation-Maximization and mixture density estimation Hidden Markov Models Bayesian Belief Networks Readings: DHS Ch 3.1-3.5, 3.9, 10.2-10.4, 3.10, 2.11 References: J. A. Bilmes, "A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models" (local copy), Technical Report TR-97-021, International Computer Science Institute, University of California, Berkeley, April 1998. G. J. McLachlan, T. Krishnan, The EM Algorithm and Extensions, John Wiley & Sons, Inc., 1997. L. R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition" (local copy), Proceedings of the IEEE, 77(2):257-286, February 1989. K. P. Murphy, "A Brief Introduction to Graphical Models and Bayesian Networks" (local copy), May 2001. E. Charniak, "Bayesian Networks Without Tears" (local copy), AI Magazine, 12(4):50-63, 1991. D. Heckerman, "A Tutorial on Learning With Bayesian Networks" (local copy), Technical Report MSR-TR-95-06, Microsoft Research, March 1995. W. L. Buntine, "Operations for Learning with Graphical Models" (local copy), Journal of Artificial Intelligence Research, 2:159-225, 1994.
Non-parametric Methods [ Slides ] (Mar 20, 22)	Topics: Density estimation Histogram-based estimation Parzen windows estimation Nearest neighbor estimation Readings: DHS Ch 4.1-4.4
Spring Break (Mar 27-31)	No class
Feature Reduction and Selection [ Slides ] (Apr 3)	Topics: Problems of dimensionality Component analysis Principal components analysis (PCA) Linear discriminant analysis (LDA) Feature selection Readings: DHS Ch 3.7-3.8, 10.13-10.14 References: L. K. Saul, S. T. Roweis, "Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds" (local copy), Journal of Machine Learning Research, 4:119-155, 2003. A. Hyvarinen, "Survey on Independent Component Analysis" (local copy), Neural Computing Surveys, 2:94-128, 1999. A. Hyvarinen, E. Oja, "Independent Component Analysis: Algorithms and Applications" (local copy), Neural Networks, 13(4-5):411-430, 2000. A. K. Jain, D. Zongker, "Feature Selection: Evaluation, Application, and Small Sample Performance" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(2):153-158, February 1997.
Non-Bayesian Classifiers [ Slides: Part 1 | Part 2 | Part 3 ] (Apr 5, 10, 12, 24)	Topics: k-nearest neighbor classifier Linear discriminant functions Support vector machines Neural networks Decision trees Readings: DHS Ch 4.5-4.6, 5.1-5.3, 5.11, 6.1-6.3, 8.1-8.3 References: M. A. Hearst, "Support Vector Machines" (local copy), IEEE Intelligent Systems, 13(4):18-28, July 1998. B. Scholkopf, C. Burges, A. J. Smola, "Introduction to Support Vector Learning" (local copy), in Advances in Kernel Methods - Support Vector Learning, MIT Press, 1999. C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition" (local copy), Data Mining and Knowledge Discovery, 2(2):121-167, June 1998. A. K. Jain, J. Mao, K. M. Mohiuddin, "Artificial neural networks: a tutorial" (local copy), IEEE Computer, 29(3):31-44, March 1996. P. F. Baldi, K. Hornik, "Learning in linear neural networks: a survey" (local copy), IEEE Transactions on Neural Networks, 6(4):837-858, July 1995. G. P. Zhang, "Neural networks for classification: a survey" (local copy), IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 30(4):451-462, November 2000. T. M. Therneau, E. J. Atkinson, "An Introduction to Recursive Partitioning Using the RPART Routines" (local copy), Technical Report 61, Department of Health Science Research, Mayo Clinic, Rochester, Minnesota, September 1997.
Unsupervised Learning and Clustering [ Slides ] (Apr 26, May 1)	Topics: Criterion functions for clustering k-means clustering Hierarchical clustering Graph-theoretic clustering Cluster validity Readings: DHS Ch 10.1, 10.6-10.7, 10.9-10.10, 10.12 References: A. K. Jain, R. C. Dubes, "Algorithms for Clustering Data" (local copy), Prentice Hall, 1988 (334 pages, 40Mb!). A. K. Jain, M. N. Murty, P. J. Flynn, "Data Clustering: A Review" (local copy), ACM Computing Surveys, 31(3):264-323, September 1999. P. Berkhin, "Survey of Clustering Data Mining Techniques" (local copy), Technical Report, Accrue Software, San Jose, CA, 2002. S. Aksoy, R. M. Haralick, "Graph-Theoretic Clustering for Image Grouping and Retrieval" (also as technical report), in IEEE International Conference on Computer Vision and Pattern Recognition, 1:63-68, Fort Collins, CO, June 1999. A. K. Jain, A. Topchy, M. H. C. Law, J. M. Buhmann, "Landscape of Clustering Algorithms" (local copy), in IAPR International Conference on Pattern Recognition, 1:260-263, Cambridge, UK, August 2004.
Algorithm-Independent Learning Issues [ Slides ] (May 3, 8)	Topics: No Free Lunch Theorem Resampling for classifier design Comparing classifiers Combining classifiers Readings: DHS Ch 9.1-9.2, 9.5-9.7 References: J. Rissanen, "Modeling by Shortest Data Description," Automatica, 14:465-471, 1978. J. Rissanen, "A universal prior for integers and estimation by minimum description length," The Annals of Statistics, 11(2):416-431, 1983. J. Kittler, M. Hatef, R. P. W. Duin, J. Matas, "On Combining Classifiers" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(3):226-239, March 1998. R. P. W. Duin, D. M. J. Tax, "Experiments with Classifier Combining Rules" (local copy), in First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, 1857:16-29, Springer, 2000. L. Breiman, "Bagging Predictors" (local copy), Machine Learning, 24(2):123-140, August 1996. R. E. Schapire, "The Boosting Approach to Machine Learning: An Overview" (local copy), in Proceedings of MSRI Workshop on Nonlinear Estimation and Classification, 2002.
Structural and Syntactic Pattern Recognition [ Slides ] (May 10, 15)	Topics: Recognition with strings Grammatical methods Graph-theoretic methods Readings: DHS Ch 8.5-8.6 References: K.-S. Fu, T. L. Booth, "Grammatical Inference: Introduction and Survey - Part I," IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(3):343-359, 1986. K.-S. Fu, T. L. Booth, "Grammatical Inference: Introduction and Survey - Part II," IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(3):360-375, 1986. L. G. Shapiro, R. M. Haralick, "A Metric for Comparing Relational Descriptions," IEEE Trans. on Pattern Analysis and Machine Intelligence, 7:90-94, 1985. S. Dance, T. Caelli, Z.-Q. Liu, Picture Interpretation: A Symbolic Approach, World Scientific, 1995. B. T. Messmer, H. Bunke, "Efficient Subgraph Isomorphism Detection: A Decomposition Approach" (local copy), IEEE Trans. on Knowledge and Data Engineering, 12(2):307-323, March/April 2000. W. J. Christmas, J. Kittler, M. Petrou, "Structural Matching in Computer Vision Using Probabilistic Relaxation" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 17(8):749-764, August 1995. R. Myers, R. C. Wilson, E. R. Hancock, "Bayesian Graph Edit Distance" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(6):628-635, June 2000. D. Conte, P. Foggia, C. Sansone, M. Vento, "Thirty years of graph matching in pattern recognition" (local copy), International Journal of Pattern Recognition and Artificial Intelligence, 18(3):265-298, May 2004.

Assignments

1. Homework assignment 1 (Due: March 6 as hardcopy in the class)
2. Homework assignment 2 (Due: April 3 as online submission)
3. Homework assignment 3 (Due: May 12 as online submission)

Late submission policy: Unless you make prior arrangements with me (before the due date), no late homework will be accepted.

Project

The purpose of the project is to enable the students to get some hands-on experience in the design, implementation and evaluation of pattern recognition algorithms by applying them to real-world problems. The objective is to try multiple algorithms for different steps of the design cycle such as feature extraction and selection, model learning and estimation, classification and evaluation, to get as high an accuracy as possible on the selected datasets.

You can use your own data from your thesis research, select datasets from the list of data resources below, or contact the instructor for data from ongoing research on multimedia analysis (including video, audio and text data) or image classification. In any case, you should get prior approval before starting your project.

You are free to use any programming language but Matlab is strongly recommended because it is very convenient for prototyping and has many tools available for pattern recognition. You can write the codes yourself or use any code that is available in the public domain. In case you use somebody else's code, you are required to know the details of the algorithms that the code implements.

You are required to work as a group, and submit a project proposal, an interim progress report, a final report written in a conference paper format, and make a poster presentation during the finals week. Tentative schedule of the project is as follows:

• Project proposal (due April 10): Submit a 1-2 page proposal that describes the problem you would like to tackle, objective of the study, proposed algorithms, hardware/software tools and data that you plan to utilize, and evaluation strategies that you plan to use. Also provide a short list of related references.
• Interim progress report (due May 8): Submit a report that describes your progress with the project and your plans for the rest of the semester.
• Final report (due May 24): Submit a readable and well-organized report that provides proper motivation for the task, proper citation and discussion of related literature, proper explanation of the details of the approach and implementation strategies, proper performance evaluation, and detailed discussion of the results. Highlight your contributions and conclusions. Also submit well-documented software with your report.
• Poster presentation (due May 25): Present your work as a poster that fits to a board of approximately 1m-by-1m. Each team member should also provide a written description of her/his own contributions to the project.

All reports and software can be submitted using the online form.

Final report guidelines:

• Follow IEEE two-column format as shown in the example and the format definition table and glossary.
• The page limit is 6 pages.
• The report should not have any page numbers, headers or footers.
• You can use IEEE's LaTeX template or Word template. (LaTeX users: Be sure to use the template's conference mode.)
• PDF submission is recommended.

All projects' final reports are available as a pdf file (Bilkent only access).

#	Projects	Members
1	Object class recognition in natural scenes	Kerem Altun, Mert Duatepe
2	Segmentation for image classification and retrieval	Demir Gokalp, Turhan O. Daybelge
3	Performance evaluation of various pattern recognition algorithms on disturbance classification for power systems	Zeynep Yucel, Erdem Ulusoy
4	Comparison of missing data handling techniques	Metin Koc, Metin Tekkalmaz
5	Three-class obstacle classification	Oguzcan Oguz, Ahmet Tolgay, Iskender Yakin
6	Information extraction using hidden Markov models	Rifat Ozcan, Serhan Tatar, Kerem Ali Ulug
7	Commercial detector in news videos	Tolga Can, Esra Ataer
8	Missing feature imputation: comparison of EM and averaging algorithms on arrhythmia data	Aysen Tunca, Bayram Boyraz
9	Make and model recognition of a car	Rifat Aras, Barkin Basarankut, Hasan Hakan Ari
10	Adjacency modeling in geospatial objects using hidden Markov model	Hayati Cam, Ozge Cavus, Emel Kaya

Grading Policy

Homework and quiz:	55%
Term project:	40%
Class participation:	5%

All grades assigned so far are available as a pdf file.

Related Links

• Previous Semesters for CS 551
  • Spring 2005
• Pattern Classification Book (Duda, Hart, Stork)
  • Book's website
  • Make sure you check the errata for the particular printing you have.
• Software Resources
  • PRTools by the Delft Pattern Recognition Group (in Matlab) (local copy)
  • Netlab Neural Network Software (in Matlab) (local copy of software and its documentation)
  • Weka Data Mining Software (in Java)
  • Bayes Net Toolbox (in Matlab)
  • Hidden Markov Model Toolbox (in Matlab)
  • SVMlight - SVM training package (in C)
  • Sequential Minimal Optimization algorithm for SVM training
  • LIBSVM - A Library for SVM (in C++ and Java, with interfaces for additional languages)
  • Numerical Recipes (in C)
  • Software resources from Pattern Recognition Information web site
  • Software resources from Kevin Murphy's web site
  • Software resources from Kernel Machines web site
  • Software resources from Kernel Methods web site
  • Software resources from American Association for Artificial Intelligence web site
  • StatLib
  • Mathtools.net Technical Computing (in Matlab, C/C++, Java)
• Matlab Tutorials
  • Matlab Primer v2
  • Matlab Tutorial by Indiana University
  • Matlab Tutorial by the University of New Hampshire
  • Matlab Tutorial by MIT
  • MathWorks - Matlab Tutorial
  • MathWorks - Student Center
  • MathWorks - HTML Documentation
• Data Resources
  • Pattern recognition related archives
  • Computer vision test images
  • UCI Machine Learning Repository
  • Labeled databases for object detection
  • Image database from the University of Washington
  • Texture database from the University of Oulu
  • Document database from the University of Oulu
  • Other databases from the University of Oulu
  • Image databases from CMU Vision and Autonomous Systems Center
  • Various other datasets from the University of Washington
  • Face databases from CMU
  • Face databases from MIT
  • Another page for face databases
  • MNIST Database of handwritten digits
  • Shape database from Brown University
  • Reuters-21578 Text Categorization Collection
  • NIST Scientific and Technical Databases
  • RISC: Repository of Information on Semi-supervised Clustering
• Others
  • Pattern Recognition Information
  • International Association for Pattern Recognition (IAPR)
  • IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence (PAMI)
  • IAPR Technical Committee 1 on Statistical Techniques in Pattern Recognition
  • IAPR Technical Committee 2 on Structural and Syntactical Pattern Recognition
  • MathWorld (an online encyclopedia of mathematical resources)
  • International Society for Bayesian Analysis
  • Statistical Learning/Pattern Recognition Glossary
  • Statistical Data Mining Tutorials
  • Kernel Machines
  • Learning with Kernels
  • Engineering Statistics Handbook
  • Introductory Statistics: Concepts, Models, and Applications
  • The Probability Web

Please send questions/suggestions to .

URI for this page is http://www.cs.bilkent.edu.tr/~saksoy/courses/cs551-Spring2006/index.html.
Last updated on April 2, 2007.