M.SC. in Computer Engineering (Without Thesis)

CE 610 | Course Introduction and Application Information

Course Name

Sparse Approximation Algorithms

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 610	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Third Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Yrd. Doç. Dr. Mehmet Türkan
Course Lecturer(s)	Yrd. Doç. Dr. Mehmet Türkan
Assistant(s)	-

Course Objectives

With this course, students will have basic knowledge on fundamentals of sparse and redundant representations first with theoretical and numerical foundations, and then practical applications originating from the theory, such that image denoising, deblurring, compression, MAP and MMSE estimations, dictionary learning, etc.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	explain fundamentals of sparse and redundant representations.
2	analyse underdetermined linear system problems with regularization techniques.
3	develop and/or apply greedy and iterative pursuit algorithms.
4	describe convex relaxation techniques and approximate solutions.
5	apply the theory of sparse and redundant representations in practical signal processing.

Course Description

Provides basic knowledge on fundamentals of sparse and redundant representations with theoretical and numerical foundations, as well as practical applications originating from the theory

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Basic introduction to sparse and redundant representations
2	Underdetermined linear systems, regularization techniques, and convexity	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 1)
3	Pursuit algorithms in practice	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 3)
4	From exact to approximate solutions	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 5)
5	Iterative-shrinkage algorithms	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 6)
6	Sparsity-seeking methods in signal processing	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 9)
7	Dictionary learning algorithms	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 12)
8	MAP and MMSE estimation	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 11)
9	Applications – Image deblurring, image denoising	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 10, Ch.14)
10	Applications – Image compression, image super-resolution	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 13, Ch.15.4)
11	Applications – Image inpainting, image cartoon/texture separation	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer 2010 (Ch. 15.2, Ch. 15.3)
12	Project presentations
13	Project presentations
14	Project presentations
15	Project presentations
16	Review of the semester

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

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation	-	-
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	-	-
Presentation / Jury	1	30
Project	1	40
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam
Total

Weighting of Semester Activities on the Final Grade	3	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		0
Study Hours Out of Class	16	3	48
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	-	-	0
Presentation / Jury	1	30	30
Project	1	80	80
Seminar / Workshop			0
Oral Exam			0
Midterms	1	19	19
Final Exam			0
		Total	225

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 Computer Engineering, evaluates, interprets and applies information.		-	-	-	-	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	-	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	-
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	-	-
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.		-	-	-	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	-	-	-	-