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	Gains sufficient knowledge in subjects specific to mathematics, natural sciences, and engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.
2	Defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose.
3	Designs a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods.
4	Selects and uses modern techniques and tools necessary for analyzing and solving complex problems encountered in engineering applications; gains the ability to use information technologies effectively.
5	Designs experiments, conducts experiments, collects data, and analyzes and interprets the results for studying complex engineering problems or research topics specific to engineering disciplines.
6	Works effectively in both disciplinary and multidisciplinary teams; gains the ability to work individually.
7	Develops effective oral and written communication skills; acquires proficiency in at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear and understandable instructions.
8	Develops awareness of the necessity of lifelong learning; gains access to information, follows developments in science and technology, and continuously renews oneself.
9	Acts in accordance with ethical principles, takes professional and ethical responsibility, and possesses knowledge of standards used in engineering applications.
10	Gains knowledge of business practices such as project management, risk management, and change management; develops awareness of entrepreneurship and innovation; possesses knowledge of sustainable development.
11	Gains knowledge of the impacts of engineering applications on health, environment, and safety in universal and societal dimensions, and the issues reflected in contemporary engineering fields; develops awareness of the legal consequences of engineering solutions.
12	Gains the ability to work in both thermal and mechanical systems fields, including the design and implementation of such systems.

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