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İzmir Ekonomi Üniversitesi

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    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
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    EEE 511
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    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;

    • Classifyartificialneuralnetworksandalgorithms in terms of theirstructures, implementationandapplicationareas,
    • Choose suitable model andlearningalgorithmsfor a specificapplication.
    • Run learningalgorithmsefficiently in a numericalsoftwareenvinronment.
    • Use artificial neural networks and learning algorithms in signal processing and control applications.
    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.

     



    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 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

    #
    		 PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    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.
    -
    -
    -
    -
    -
    2

    To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.
    -
    -
    -
    -
    -
    3

    To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.
    -
    -
    -
    -
    -
    4

    To be able to conduct basic research and write and publish articles in related conferences and journals.
    -
    -
    -
    -
    -
    5

    To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.
    -
    -
    -
    -
    -
    6

    To be able to manage decision-making processes in industrial systems.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     

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