Department of Computer Engineering

CE 322 | Course Introduction and Application Information

Course Name

Pattern Recognition

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 322	SPRING	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	-
National Occupational Classification Code	-
Course Coordinator	-
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives

The course focuses on the theory and applications of pattern recognition. The topics include an overview of the problem of pattern classification, feature extraction, object recognition, statistical decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO5	will be able to apply pattern recognition techniques using computer toolboxes.				X
LO4	will be able to compare different pattern recognition techniques.				X
LO3	will be able to describe main issues involved in pattern recognition system design.				X
LO2	will be able to explain main approaches in statistical and syntactic pattern recognition.					X
LO1	will be able to design basic and advanced pattern recognition systems.				X

Course Description

Learning and adoption, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, sufficient statistics, non-parametric techniques, linear discriminants, algorithm independent machine learning, classifiers, unsupervised learning, clustering.

Related Sustainable Development Goals

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction to Pattern Recognition, Learning and Adoption	Chapter 1. Section 1.1-1.6. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO1
2	Bayesian Decision Theory	Chapter 2. Section 2.1-2.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO2
3	Discriminant Functions	Chapter 2. Section 2.5,2.6, 2.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO1
4	Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation, Sufficient Statistics	Chapter 3. Section 3.1-3.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO1
5	Non-Parametric Techniques	Chapter 4. Section 4.1-4.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO2
6	Linear Discriminant Functions	Chapter 5. Section 5.1-5.8. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO2
7	Ara-sınav		-
8	Non-Metric Methods	Bölüm 8. Kısım 8.1-8.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO3
9	Algorithm-Independent Machine Learning	Bölüm 9. Kısım 9.1-9.3. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO4
10	Algorithm-Independent Machine Learning – Resampling	Bölüm 9. Kısım 9.4,9.5. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO4
11	Algorithm-Independent Machine Learning – Classifiers	Bölüm 9. Kısım 9.6,9.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO4
12	Unsupervised Learning and Clustering	Bölüm 10. Kısım 10.1-10.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO4
13	Unsupervised Learning and Clustering	Bölüm 10. Kısım 10.5-10.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.	LO4
14	Project Presentations		LO5
15	Semester Review		-
16	Final Exam		-

Course Notes/Textbooks	Duda. R.O.Hart. P.E. and Stork. D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
Suggested Readings/Materials	Bishop. C. M. Pattern Recognition and Machine Learning. Springer. 2007 Marsland. S. Machine Learning: An Algorithmic Perspective. CRC Press. 2009. (Also uses Python.) Theodoridis. S. and Koutroumbas. K. Pattern Recognition. Edition 4. Academic Press. 2008.

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO5	LO4	LO3	LO2	LO1
Final Exam	1	40		X	X	X	X
Midterm	1	30			X	X	X
Project	1	20	X	X	X	X	X
Homework / Assignments	1	10	X	X		X	X
Total	4	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	2	28
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	1	10	10
Presentation / Jury	-	-	-
Project	1	20	20
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	20	20
Final Exam	1	24	24
		Total	150

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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