GRADUATE SCHOOL
M.SC. In Industrial Engineering (With Thesis)
EEE 511 | Course Introduction and Application Information
| Course Name |
Artificial Neural Networks for Signal Processing and Control
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Code
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Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
EEE 511
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
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| Course Type |
Elective
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| Course Level |
Second Cycle
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The course aims the students: i) to know basic artificial neural networks models and learning algorithms and ii) to use artificial neural networks models and associated learning algorithms for signal processing and control applications. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | Artificial neural networks architectures and learning algorithms. Multi layer perceptron, radial basis function networks and support vector machines. Regression / function approximation, classification and clustering. Artificial neural networks for signal processing, filtering and pattern recognition. Artificial neural networks for system identification and control. |
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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 |
| 1 | Biological motivation. Historical remarks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 2 | Taxonomy of artificial neural networks and learning algorithms. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 3 | Linear adaptive element, least mean square algorithm and convergence analysis. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 4 | Discrete perceptron, perceptron learning rule and convergence analysis | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 5 | Multi-layer perceptron, back propagation algorithm and its variants with their convergence analyses, overfitting. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 6 | Radial basis function networks, design by input and input-output clustering | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 7 | Support vector machines, Mercer theorem, kernel representation, Lagrange multipliers | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 8 | Generalization,Vapnik-Chervonenkis dimension. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 9 | 1. Midterm | |
| 10 | Pattern recognition, feature extraction, dimension and data reduction by artificial neural networks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 11 | 1-Dimensional biomedical signal processing by artificial neural networks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 12 | Biomedical image processing by artificial neural networks | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 13 | 2. Midterm | |
| 14 | Systems identification by artificial neural networks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 15 | Artificial neural networks based controller design. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes., |
| 16 | Review of the Semester |
| Course Notes/Textbooks | The textbook referenced above and lecture notes |
| Suggested Readings/Materials | Related Books and Research Papers |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application |
6
|
60
|
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
2
|
40
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
8
|
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
|
2
|
32
|
| Study Hours Out of Class |
15
|
4
|
60
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
2
|
42
|
84
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
0
|
||
| Total |
224
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
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#
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Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
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| 1 | To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems. |
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| 2 | To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
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| 3 | To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
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| 4 | To be able to conduct basic research and write and publish articles in related conferences and journals. |
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| 5 | To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
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| 6 | To be able to manage decision-making processes in industrial systems. |
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| 7 | To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary. |
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| 8 | To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering. |
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| 9 | To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria. |
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| 10 | To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest