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	Bachelor’s Degree (First Cycle)
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	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems.				X
2	Ability to formulate, and solve complex mechatronics engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.				X
3	Ability to design a complex mechatronics engineering system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.			X
4	Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in mechatronics engineering and robot technology practices; ability to employ information technologies effectively.				X
5	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex mechatronics engineering and robot technology problems or research questions.			X
6	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.				X
7	Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.		X
8	Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself		X
9	a-) Knowledge on behavior according to ethical principles, professional and ethical responsibility b-) Knowledge on standards used in engineering practices.
10	a-) Knowledge about business life practices such as project management, risk management, and change management b-) Awareness in entrepreneurship, innovation; knowledge about sustainable development.
11	Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
12	Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions in the field of mechatronics engineering.
13	Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planning, improving or changing the norms with a criticism.

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