Course Information

Course Name	Turkish	İstatistiksel Örüntü Analizi ve Sınıflandırma
	English	Statis.Pattern .Analy.&Classif
Course Code	TEL 613E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	1	3	3	-	-
Course Language		English
Course Coordinator		Hülya Yalçın
Course Objectives		To teach fundamentals of Statistical Pattern Analysis and Classification concepts and algorithms, To teach students programming techniques for machine learning and to encourage them to incorporate these techniques into their own research.
Course Description		The statistical theory of pattern analysis, including both parametric and nonparametric approaches to classification. Multidimensional probability distributions. Classification with likelihood functions and Bayesian estimation, linear discriminant functions, density estimation, supervised and unsupervised classification, performance estimation, and classification using sequential and contextual information, including Markov and hidden Markov models. Gaussian mixture models. Eigenvalue decomposition. Feature reduction, combination of classifiers and sensor fusion. Application areas include image and video processing
Course Outcomes		M.Sc./Ph.D. students who successfully pass this course gain knowledge, skill and competency in the following subjects; 1. Statistical classification of multidimensional signals 2. Linear classifiers. 3. Multidimensional estimation 4. Parametric nonparametric statistical classification methods 5. Nonlinear classifiers. 6. Unsupervised classification.
Pre-requisite(s)
Required Facilities
Other
Textbook
Other References		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.

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