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Ph.D. In Computer Engineering
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 | ||||||
| Course Lecturer(s) | ||||||
| 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 |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
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 | 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
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 | Understands and applies the foundational theories of Computer Engineering in a high level. |
-
|
-
|
-
|
X
|
-
|
|
| 2 | Possesses a great depth and breadth of knowledge about Computer Engineering including the latest developments. |
-
|
-
|
-
|
-
|
X
|
|
| 3 | Can reach the latest information in Computer Engineering and possesses a high level of proficiency in the methods and abilities necessary to comprehend it and conduct research with it. |
-
|
-
|
-
|
-
|
X
|
|
| 4 | Conducts a comprehensive study that introduces innovation to science and technology, develops a new scientific procedure or a technological product/process, or applies a known method in a new field. |
-
|
-
|
-
|
-
|
-
|
|
| 5 | Independently understands, designs, implements and concludes a unique research process in addition to managing it. |
-
|
X
|
-
|
-
|
-
|
|
| 6 | Contributes to science and technology literature by publishing the output of his/her academic studies in respectable academic outlets. |
-
|
-
|
X
|
-
|
-
|
|
| 7 | Interprets scientific, technological, social and cultural developments and relates them to the general public with a commitment to scientific objectivity and ethical responsibility. |
-
|
-
|
-
|
-
|
-
|
|
| 8 | Performs critical analysis, synthesis and evaluation of ideas and developments in Computer Engineering. |
-
|
X
|
-
|
-
|
-
|
|
| 9 | Performs verbal and written communications with professionals as well as broader scientific and social communities in Computer Engineering, by using English at least at the European Language Portfolio C1 General level, performs written, oral and visual communications and discussions in a high level. |
-
|
-
|
-
|
-
|
-
|
|
| 10 | Develops strategies, policies and plans about systems and topics that Computer Engineering uses, and interprets the outcomes. |
-
|
-
|
X
|
-
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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