TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Assistant Professor Çağatay Berke Erdaş
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Learn basic principles of pattern recognition. 2) Get practice on developing and using PR programs. 3) Get ability to PR techniques in problem solving.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	PR fundamentals Feature Reduction. Supervised classification. Perceptron Algorithms. Linear Discriminants. Nearest Neigborhood. Maximum Likelihood Estimation. Bayesian inference. Suppert Vector Machines. Hidden Markov Models. Unsupervised methods. K-means. Hierarchical clustering. Recent challenges
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to pattern recognition 2nd Week Data and feature fundamentals 3rd Week Data preprocessing and normalization 4th Week Feature extraction 5th Week Feature selection, sample segmentation 6th Week Clustering fundamentals 7th Week Classification basics 8th Week Midterm Exam 9th Week Regression basics 10th Week Performance evaluation metrics 11th Week Trend topics in pattern recognition - I 12th Week Trend topics in pattern recognition - II 13th Week Presentations 14th Week Presentations
RECOMENDED OR REQUIRED READING	1. Pattern Classification 2nd. Edition., R.O. Duda, P.E. Hart & D.G. Stork, J. Wiley Inc., 2001
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Project
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 2 30 Project 1 30 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 11 154 Preparation for Final exam 1 69 69 Course hours 14 3 42 Preparation for Midterm exam 1 44 44 Laboratory (including preparation) Final exam 1 2 2 Homework Total Workload 313 Total Workload / 30 10,43 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No