Personnel
Instructor:  Selim Aksoy 

Office:  EA 423 
Email: 
Course Information
Schedule:  Mon 9:4010:30, Wed 9:4011:30 (EA 502) 

Office hours:  Wed 13:4015:30 
Mailing list:  http://retina.cs.bilkent.edu.tr/mailman/listinfo/cs551spring2008 
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, 2nd edition, John Wiley & Sons, Inc., 2002.
 C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
 S. Theodoridis, K. Koutroumbas, Pattern Recognition, 3rd edition, Academic Press, 2006.
 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, 2003.
 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 11, 13) 
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Bayesian Decision Theory[ Slides ] (Feb 18, 20, 25) 
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Parametric Models[ Slides: Part 1  Part 2  Part 3  Part 4 ] (Feb 27, Mar 3, 5, 10, 12, 17, 19, 24) 
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Nonparametric Methods[ Slides ] (Mar 26, 31) 
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Feature Reduction and Selection[ Slides ] (Apr 2) 
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NonBayesian Classifiers[ Slides: Part 1  Part 2  Part 3 ] (Apr 7, 9, 14, 16) 
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Unsupervised Learning and Clustering[ Slides ] (Apr 28, 30, May 5) 
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AlgorithmIndependent Learning Issues[ Slides ] (May 7, 12) 
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Structural and Syntactic Pattern Recognition[ Slides ] (May 14, 20) 
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Assignments
 Homework assignment 1 (Due: March 19, 2008 as hardcopy in the class)
 Homework assignment 2 (Due: April 17, 2008 as online submission)
 Homework assignment 3 (Due: May 19, 2008 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 handson experience in the design, implementation and evaluation of pattern recognition algorithms by applying them to realworld 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 at the RETINA group (including image, video, audio and text data). 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 properly cite its source and 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 presentation during the finals week. Tentative schedule of the project is as follows:
 Project proposal (due April 15, 2008): Submit a 12 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 12, 2008): Submit a report that describes your progress with the project and your plans for the rest of the semester.
 Final report (due May 27, 2008): Submit a readable and wellorganized 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 welldocumented software with your report.
 Presentation (due May 29, 2008): Make a ~10 minute presentation of your work to the class. 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 Computer Society twocolumn format as described in their examples and templates.
 The page limit is 6 pages.
 The report should not have any page numbers, headers or footers.
 PDF submission is recommended.
#  Projects  Members 

1  Exploitation of feature selection methods in hierarchical image segmentation  Firat Kalaycilar, Asli Kale, Daniya Zamalieva 
2  Classification of cardiac arrhythmia using nominal and continuous features  Burak Ozek 
3  Using graphbased method for semantic scene classification  Dogan Altunbay, Onur Kucuktunc 
4  Motion recognition with gyroscopes  Derya Gol, Erdem Sahin 
5  Speaker detection  Hidayet Aksu, Ethem F. Can, Mahmut Yavuzer 
6  Feature selection for scene classification  R. Gokberk Cinbis, A. Osman Ulusoy 
7  Image clustering  Sare Gul Sevil, Can Sardan, Hilal Zitouni 
8  Speech recognition for expression evaluation  Enver Kayaaslan, Sitar Kortik, Tugba Yildiz 
Grading Policy
Homework and quiz:  55% 
Term project:  40% 
Class participation:  5% 
Related Links
 Previous semesters for CS 551
 Duda, Hart, Stork book
 Book's website
 Make sure you check the errata for the particular printing you have.
 Webb book
 Bishop book
 Theodoridis and Koutroumbas book
 Hastie, Tibshirani, Friedman book
 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
 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
 Reuters21578 Text Categorization Collection
 NIST Scientific and Technical Databases
 RISC: Repository of Information on Semisupervised 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