| Course Name | Intoduction to Sparse Representations |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| CE 462 | Fall/Spring | 3 | 0 | 3 | 5 |
| Prerequisites | None | |||||
| Course Language | English | |||||
| Course Type | Elective | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | ||||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course seeks a place on solid foundations to introduce the basic theoretical and numerical concepts of sparse representation algorithms, and to illustrate their practical applications. |
| Learning Outcomes | The students who succeeded in this course;
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| Course Description | Provides introductory knowledge on the basics of sparse representations with theoretical and numerical aspects, and practical applications in real life. |
| Related Sustainable Development Goals | |
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| Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 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 | |
| Suggested Readings/Materials |
| Semester Activities | Number | Weigthing |
| Participation | 1 | 10 |
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | 3 | 30 |
| Presentation / Jury | 1 | 35 |
| Project | 1 | 25 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade | 6 | 100 |
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
| Study Hours Out of Class | 8 | 3 | 24 |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | 3 | 8 | |
| Presentation / Jury | 1 | 12 | |
| Project | 1 | 42 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | |||
| Final Exams | |||
| Total | 150 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems | |||||
| 2 | To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. | |||||
| 3 | To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose. | |||||
| 4 | To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively. | |||||
| 5 | To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems. | |||||
| 6 | To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually. | |||||
| 7 | To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. | |||||
| 8 | To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions. | |||||
| 9 | To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications. | |||||
| 10 | To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
| 11 | Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1) | |||||
| 12 | To be able to use the second foreign language at intermediate level. | |||||
| 13 | To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
