Department of Computer Engineering

CE 462 | Course Introduction and Application Information

Course Name

Intoduction to Sparse Representations

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 462	FALL	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	Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Doç. Dr. Mehmet Türkan
Course Lecturer(s)	Doç. Dr. Mehmet Türkan
Assistant(s)	-

Course Objectives

This course serves as an introduction to sparse representation methods, providing a strong theoretical and numerical foundation for these techniques and enabling their application in practical scenarios.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Explain the fundamental elements of sparse representation methods.			X
LO2	Implement greedy pursuit algorithms.				X
LO3	Analyze underdetermined linear systems.				X
LO4	Describe convex relaxation techniques and approximate computation solutions.			X
LO5	Apply the theory of sparse representation methods to practical problems.					X

Course Description

Providing the fundamental elements of sparse representation methods both theoretically and numerically, and enabling their use in real-life applications.

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 sparse and redundant representation methods; mathematical foundations	-	-
2	Underdetermined linear systems, regularization techniques, and convexity	Chapter 1. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO1
3	Practical pursuit algorithms	Chapter 3. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO2
4	Transition from exact solutions to approximate solutions	Chapter 5. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO3
5	Iterative shrinkage algorithms	Chapter 6. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO3
6	Sparsity-seeking methods in signal processing	Chapter 9. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO3
7	Dictionary learning algorithms	Chapter 12. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO4
8	Midterm Exam	-	-
9	MAP and MMSE estimators	Chapter 11. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO4
10	Application examples – image deblurring, noise filtering, inpainting, cartoon/texture decomposition, compression, super-resolution	Chapter 10-13-14-15. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO5
11	Application examples – image deblurring, noise filtering, inpainting, cartoon/texture decomposition, compression, super-resolution	Chapter 10-13-14-15. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO5
12	Application examples – image deblurring, noise filtering, inpainting, cartoon/texture decomposition, compression, super-resolution	Chapter 10-13-14-15. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, ISBN: 978-1-4419-7010-7	LO5
13	Project presentations	-	LO5
14	Project presentations	-	LO5
15	Project presentations	-	LO5
16	Final Exam	-	-

Course Notes/Textbooks	M. Elad Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing Springer 2010 ISBN: 978-1-4419-7010-7
Suggested Readings/Materials	-

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	4	40	X	X	X	X	X
Presentation / Jury	1	20	X	X	X	X	X
Project	1	40	X	X	X	X	X
Total	6	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	4	6	24
Presentation / Jury	1	5	5
Project	1	45	45
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	-	-	-
Final Exam	-	-	-
		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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