M.SC. in Electrical and Electronics Engineering (Without Thesis)

EEE 502 | Course Introduction and Application Information

Course Name

Pattern Recognition

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 502	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Türker İNCE
Course Lecturer(s)	Prof. Dr. Türker İNCE
Assistant(s)	-

Course Objectives

This course covers foundations of pattern recognition algorithms and their applications. Topics covered include statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	learn to use and discuss the basic techniques/algorithms of the field,
2	have knowledge of the advantages and limitations of different pattern recognition algorithms,
3	be able to evaluate potential applications of pattern recognition techniques,

Course Description

Pattern recognition algorithms and their applications, statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering.

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction to Pattern Recognition	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 1)
2	Bayesian Decision Theory	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 2)
3	Maximum Likelihood and Bayesian Estimation	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 3)
4	Nonparametric Techniques	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 4)
5	Linear Discriminant Functions	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 5)
6	Feature Selection and Dimension Reduction Techniques	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 5)
7	Multilayer Neural Networks	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6)
8	Midterm
9	Multilayer Neural Networks	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6)
10	Radial Basis Function Networks	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6)
11	Stochastic Methods	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 7)
12	Nonmetric Methods	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 8)
13	Unsupervised Learning and Clustering	Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 10)
14	In-class Presentations
15	In-class Presentations
16	Review of the Semester

Course Notes/Textbooks	The textbook referenced above and course slides
Suggested Readings/Materials	Related Research Papers

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3
Participation
Laboratory / Application	6	60
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	2	40
Seminar / Workshop
Oral Exams
Midterm
Final Exam
Total

Weighting of Semester Activities on the Final Grade	8	100
Weighting of End-of-Semester Activities on the Final Grade
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16	2	32
Study Hours Out of Class	15	4	60
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	2	42	84
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam			0
		Total	224

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering, evaluates, interprets and applies information.		-	X	-	-	-
2	Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations.		-	-	X	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	X	-
5	Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	X	-	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		X	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics engineering applications.		X	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		X	-	-	-	-