Pattern Recognition (EE448) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Pattern Recognition	EE448	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Natural & Applied Sciences Master's Degree
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Discussion, Drill and Practice.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	1. Instill in the students an understanding of where Pattern Recognition sits in the hierarchy of artificial intelligence and soft computing techniques 2. Develop expertise in various unsupervised learning algorithms such as clustering techniques (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation 3. Provide the student with the ability to apply these techniques in exploratory data analysis
Course Learning Outcomes	The students who succeeded in this course; Ability to formulate and describe various applications in pattern recognition Ability to understand the Bayesian approach to pattern recognition Ability to mathematically derive, construct, and utilize Bayesian based classifiers and non-Bayesian based classifiers both theoretically and practically Ability to identify the strengths and weakness of different types of classifiers Ability to validate and assess different clustering techniques Ability to apply various dimensionality reduction methods whether through feature selection or feature extraction Ability to use computer tools (such as Matlab) in developing and testing pattern recognition algorithms Ability to complete a term project
Course Content	Introduction to the theory of pattern recognition, Bayesian decision theory, Maximum likelihood estimation, Nonparametric estimation, Linear discriminant functions, Support vector machines, Neural networks, Unsupervised learning and Clustering, Applications such as handwriting recognition, lipreading, geological analysis, medical data processing, d

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction to Pattern Recognition	Glance this week’s topics from the course book
2	Classifiers based on Bayesian decision theory	Review last week and glance this week’s topics from your course supplements
3	Classifiers based on Bayesian decision theory	Review last week and glance this week’s topics from your course supplements
4	Linear classifiers	Review last week and glance this week’s topics from your course supplements
5	Nonlinear classifiers	Review last week and glance this week’s topics from your course supplements
6	Nonlinear classifiers	Review last week and glance this week’s topics from your course supplements
7	Classifier combination	Review last week and glance this week’s topics from your course supplements
8	Feature selection	Review last week and glance this week’s topics from your course supplements
9	Feature generation	Review last week and glance this week’s topics from your course supplements
10	Feature generation	Review last week and glance this week’s topics from your course supplements
11	Clustering Algorithms, Multidimensional scaling	Review last week and glance this week’s topics from your course supplements
12	Clustering Algorithms, Multidimensional scaling	Review last week and glance this week’s topics from your course supplements
13	Case studies: Image and speech processing	Review last week and glance this week’s topics from your course supplements
14	Case studies: Image and speech processing	Review last week and glance this week’s topics from your course supplements

Sources

Course Book	1. Pattern Recognition, S.Theodoridis and K.Koutroumbas,4th Ed., Academic Press, 2009.
Other Sources	2. Pattern Classification, R.O.Duda, P.E.Hart and D.G.Stork, John Wiley, 2001.
	3. Pattern Recognition and Machine Learning, C.M.Bishop, Springer, 2006.
	4. Introduction to Pattern Recognition A Matlab Approach, S.Theodoridis, A.Pikrakis, K.Koutroumbas, D.Cavouras, Academic Press, 2010.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	3	15
Presentation	-	-
Project	1	20
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	25
Final Exam/Final Jury	-	-
Toplam	5	60

Percentage of Semester Work	55
Percentage of Final Work	45
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems.
2	Develop solutions using different technologies, software architectures and life-cycle approaches.
3	An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices.
4	An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5	Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects.
6	An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
7	An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8	Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
9	An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10	Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application	4	4	16
Special Course Internship
Field Work
Study Hours Out of Class	14	3	42
Presentation/Seminar Prepration	1	4	4
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	2	2	4
Prepration of Final Exams/Final Jury	1	3	3
Total Workload			117