Department of Electrical and Electronics 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.	1.5		X
LO2	Implement greedy pursuit algorithms.	1.6			X
LO3	Analyze underdetermined linear systems.	2			X
LO4	Describe convex relaxation techniques and approximate computation solutions.	1.5		X
LO5	Apply the theory of sparse representation methods to practical problems.	4				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
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics
	2	Science
	3	Basic Engineering
	4	Computation
	5	Related engineering discipline-specific topics		LO1 LO4
	6	The ability to apply this knowledge to solve complex engineering problems			LO2
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.				LO3
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.					LO5
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
	1	Knowledge of business practices such as project management and economic feasibility analysis
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.