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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Electrical and Electronics Engineering (Without 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
    X
    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

    Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering, evaluates, interprets and applies information.
    -
    -
    X
    -
    -
    2

    Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations.
    -
    -
    -
    X
    -
    3

    Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.
    -
    -
    -
    -
    X
    4

    Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    X
    -
    5

    Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    X
    -
    6

    Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.
    -
    -
    -
    X
    -
    7

    Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.
    -
    -
    X
    -
    -
    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.
    -
    -
    -
    X
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    -
    X
    -
    10

    Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    X
    -
    -
    11

    Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics engineering applications.
    -
    -
    X
    -
    -
    12

    Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    -
    X
    -
    -

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

     

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