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

Announcements

1. (Feb 5) Course page is online.
2. (Feb 5) Syllabus is available.
3. (Feb 5) Slides for Introduction to Pattern Recognition are available.
4. (Feb 11) Slides for Bayesian Decision Theory are available.
5. (Feb 24) First part of the slides for Parametric Models is available.
6. (Feb 24) Second part of the slides for Parametric Models is available.
7. (Feb 24) Third part of the slides for Parametric Models is available.
8. (Mar 3) Homework assignment 1 is available.
9. (Mar 18) Slides for Non-parametric Methods are available.
10. (Mar 18) First part of the slides for Probabilistic Graphical Models is available.
11. (Mar 18) Second part of the slides for Probabilistic Graphical Models is available.
12. (Mar 18) Third part of the slides for Probabilistic Graphical Models is available.
13. (Apr 2) Homework assignment 2 is available.
14. (Apr 7) Slides for Feature Reduction and Selection are available.
15. (Apr 15) First part of the slides for Non-Bayesian Classifiers is available.
16. (Apr 15) Second part of the slides for Non-Bayesian Classifiers is available.
17. (Apr 28) Homework assignment 3 is available.
18. (May 6) Slides for Structural and Syntactic Pattern Recognition are available.

Personnel

Instructor:	Selim Aksoy
Office:	EA 422
Email:

Course Information

Schedule:	Tue 13:40-15:30, Thu 15:40-17:30 (EA 502)
Office hours:	TBD (EA 422)
Prerequisites:	Probability theory, statistics, linear algebra

Texts

• K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000.
• S. Theodoridis, K. Koutroumbas, Pattern Recognition, 3rd edition, Academic Press, 2006.
• D. Koller, N. Friedman, Probabilistic Graphical Models: Principals and Techniques, MIT Press, 2009.
• A. Webb, Statistical Pattern Recognition, 2nd edition, John Wiley & Sons, Inc., 2002.
• 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 6, 11)	Topics: Pattern recognition systems The design cycle An example 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. C. M. Grinstead, J. L. Snell, Introduction to Probability (local copy), American Mathematical Society, 1997. R. C. Gonzales, R. E. Woods, "Brief Review of Matrices, Vectors, Probability and Random Variables" (local copy), 2002.
Bayesian Decision Theory [ Slides ] (Feb 13, 18, 20)	Topics: Modeling using continuous and discrete features Discriminant functions The Gaussian density Error estimation References: Receiver Operating Characteristic Curves: An Introduction An ROC applet Another ROC applet T. Fawcett, "An Introduction to ROC Analysis" (local copy), Pattern Recognition Letters, 27(8):861-874, June 2006.
Parametric Models [ Slides: Part 1 | Part 2 | Part 3 ] (Feb 25, 27, Mar 4, 11, 13, 18)	Topics: Maximum-likelihood estimation Bayesian estimation Expectation-Maximization and mixture density estimation Hidden Markov Models 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.
Non-parametric Methods [ Slides ] (Mar 20)	Topics: Density estimation Histogram-based estimation Parzen windows estimation Nearest neighbor estimation
Probabilistic Graphical Models [ Slides: Part 1 | Part 2 | Part 3 ] (Mar 25, 27, Apr 1, 3)	Topics: Directed graphical models - Bayesian networks Undirected graphical models Inference using graphical models Learning graphical models References: 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.
Feature Reduction and Selection [ Slides ] (Apr 8, 10, 15)	Topics: Problems of dimensionality Component analysis Principal components analysis (PCA) Linear discriminant analysis (LDA) Manifold learning Feature selection References: J. B. Tenenbaum, V. de Silva, J. C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction" (local copy), Science, 290:2319-2323, 2000. S. T. Roweis, L. K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding" (local copy), Science, 290:2323-2326, 2000. 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. L. Cayton, "Algorithms for Manifold Learning" (local copy), Technical Report CS2008-0923, University of California, San Diego, 2005. L. J. P. van der Maaten, E. O. Postma, H. J. van den Herik, "Dimensionality Reduction: A Comparative Review" (local copy), Technical Report TiCC-TR 2009-005, Tilburg University, 2009. Resources on manifold learning 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. Feature selection demonstrations
Non-Bayesian Classifiers [ Slides: Part 1 | Part 2 ] (Apr 17)	Topics: k-nearest neighbor classifier Linear discriminant functions Support vector machines 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.
Structural and Syntactic Pattern Recognition [ Slides ] (April 22, 24, May 6, 8, 13, 15)	Topics: Graph-theoretic methods Recognition with strings Grammatical methods Readings: DHS Ch 8.5-8.6 References: 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. R. P. W. Duin, "Structural Inference of Sensor-Based Measurements" (local copy), Pierre Devijver Award Lecture at S+SSPR 2006. R. P. W. Duin, "Representation Related Problems in Pattern Recognition" (local copy), Invited talk at Joint Meeting of the British Statistical Society and the Royal Classification Society, 2006. H. Bunke, "Graph Classification Based on Dissimilarity Space Embedding" (local copy), Invited talk at S+SSPR 2008. 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.

Exam

• Midterm exam will be held at EA 502 during 13:40-15:30 (class hours) on April 29, 2014. The exam will cover all topics from the beginning of the semester until the end of the non-Bayesian classifiers chapter.

Assignments

1. Homework assignment 1: description (Due: March 24, 2014 as online submission)
2. Homework assignment 2: description | data (Due: April 21, 2014 as online submission)
3. Homework assignment 3: description | data (Due: May 18, 2014 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 an acceptable accuracy on the selected data sets. An acceptable accuracy will be problem dependent but needs to be measured using formal quantitative methods.

You can use your own data from your thesis research, select data sets from the list of data resources below, or contact the instructor. 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 in groups of two, 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 14, 2014): 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 5, 2014): Submit a report that describes your progress with the project and your plans for the rest of the semester.
• Final report (due May 23, 2014): 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.
• Presentation (due May 26, 2014): Make a ~10 minute presentation of your work to the class. Each student is expected to attend all presentations. 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:

• The reports are expected to be 6 pages and must follow the IEEE Computer Society two-column format as described in their templates. Try to follow the format as closely as possible.
• PDF submission is required.

Grading Policy

Midterm exam:	25%
Homework:	40%
Project:	30%
Class participation:	5%

Related Links

• Previous semesters for CS 551
  • Fall 2012
  • Spring 2012
  • Spring 2011
  • Spring 2010
  • Spring 2009
  • Spring 2008
  • Spring 2007
  • Spring 2006
  • Spring 2005
• Duda, Hart, Stork book
  • Book's website
  • Make sure you check the errata for the particular printing you have.
• Theodoridis and Koutroumbas book
  • Book's website
• Bishop book
  • Book's website
• Koller and Friedman book
  • Book's website
• Webb book
  • Book's website
• Hastie, Tibshirani, Friedman book
  • Book's website
• Software resources
  • PRTools by the Delft Pattern Recognition Group (in Matlab) (local copy)
  • Matlab Toolbox for Dimensionality Reduction
  • 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 data sets 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 Education Committee Resources (Tutorials, data sets, codes, etc.)
  • 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-Spring2014/index.html.
Last updated on May 6, 2014.