TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Assistant Professor Hakan Tora
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Learn basic principles of pattern recognition 2) Learn machine learning methods 3) Get practice on developing and using PR programs 4) Get ability to PR techniques in probelm solving
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	An introduction to the machine recognition of one, two or higher dimensional patterns. Statistical and linguistic approaches. Survey of application areas. Bayes Decision Theory, decision bounderies, classifiers and discriminant functions. Estimation of parameters. Clustering. Feature selection. Structural approaches to pattern recognition. Neural network recognizers. Applications.
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	Lecture,Questions/Answers,Problem Solving,Experiment,Practice,Project,Report Preparation,Presentation,Other
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 10 Quiz 3 10 Project 1 10 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 1,5 1,5 Preparation for Quiz 4 1 4 Individual or group work Preparation for Final exam 1 30 30 Course hours 14 4 56 Preparation for Midterm exam 1 20 20 Laboratory (including preparation) Final exam 1 2 2 Homework 3 10 30 Quiz 4 ,5 2 Total Workload 145,5 Total Workload / 30 4,85 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No