Course Information

Course Name	Turkish	Örüntü Tanıma
	English	Pattern Recognition
Course Code	BLG 601E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	2	3	3	-	-
Course Language		English
Course Coordinator		Zehra Çataltepe
Course Objectives		This course will provide a deeper theoretical understanding of methods and issues in machine learning/pattern recognition. Students will also gain theoretical and practical experience through programming exercises and projects.
Course Description		Introduction, mathematical preliminaries; Pattern Recognition basics; Probability Distributions; Linear Models for Regression; Linear Models for Classification; Neural Networks; Kernel Methods; Sparse Kernel Machines; Graphical Models; Mixture Models and EM; Continuous Latent Variables ; Combining Models; Sequential Data; Approximate Inference; Sampling Methods
Course Outcomes		I.Ability to propose a pattern recognition method for a specific problem II.Ability to analyze performance of different pattern recognition methods III.Ability to combine outputs of different pattern recognition methods IV. Ability to understand the theoretical foundations and workings of different pattern recognition methods V. Ability to modify a pattern recognition method to solve a new problem.
Pre-requisite(s)		Probability and Statistics, master level machine learning/pattern recognition course, ability to program in a language such as java or matlab, ability to read and write scientific articles in English.
Required Facilities
Other		coursebook, computer
Textbook		CHRIS BISHOP, PATTERN RECOGNITION AND MACHINE LEARNING, SPRINGER 2006.
Other References		ETHEM ALPAYDIN, “INTRODUCTION TO MACHINE LEARNING (ADAPTIVE COMPUTATION AND MACHINE LEARNING)”, THE MIT PRESS, 2004 PATTERN CLASSIFICATION, 2ND EDITION, RICHARD O. DUDA, PETER E. HART, AND DAVID G. STORK, 2000, WILEY

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