İzmir Ekonomi Üniversitesi
  • TÜRKÇE

  • GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    Course Structure Diagram with Credits

    To see the course details (such as objectives, learning outcomes, content, assessment and ECTS workload), click the relevant Course Code given in the table below.

    1. Year Fall Semester
    Code Pre. Course Name Theory App/Lab Local Credits ECTS
    ELEC 001 Elective Course I 3 0 3 7.5
    ELEC 002 Elective Course II 3 0 3 5
    ELEC 003 Elective Course III 3 0 3 5
    ELEC 004 Elective Course IV 3 0 3 5
    GS 593 Research Design and Methods in Engineering 3 0 3 7.5
    Total : 30
    1. Year Spring Semester
    Code Pre. Course Name Theory App/Lab Local Credits ECTS
    CE 516 Advanced Computer Networks & Communication 3 0 3 7.5
    CE 597 Term Project 0 0 0 2.5
    ELEC 005 Elective Course V 3 0 3 5
    ELEC 006 Elective Course VI 3 0 3 5
    ELEC 007 Elective Course VII 3 0 3 5
    ELEC 008 Elective Course VIII 3 0 3 5
    Total : 30
    Elective Courses
    Code Pre. Course Name Theory App/Lab Local Credits ECTS
    CE 513 Advanced Operating Systems 3 0 3 7.5
    CE 518 Advanced Computing Theory 3 0 3 7.5
    CE 531 Machine Learning 3 0 3 7.5
    CE 533 Artificial Intelligence 3 0 3 7.5
    CE 534 Intelligent Agents and Multi Agent Planning 3 0 3 7.5
    CE 536 Human-Computer Interaction 3 0 3 7.5
    CE 601 Advanced Algorithms 3 0 3 7.5
    CE 602 Advanced Computer Architecture 3 0 3 7.5
    CE 603 Advanced Distributed Database Systems 3 0 3 7.5
    CE 604 Advanced Computer Graphics 3 0 3 7.5
    CE 605 Wireless Sensor Networks 3 0 3 7.5
    CE 606 Video Coding and Decoding 3 0 3 7.5
    CE 607 Information Security 3 0 3 7.5
    CE 608 Formal Specification and Verification of Concurrent Systems 3 0 3 7.5
    CE 609 Advanced Numerical Analysis 3 0 3 7.5
    CE 610 Sparse Approximation Algorithms 3 0 3 7.5
    CE 611 Design Patterns and Code Refactoring 3 0 3 7.5
    CE 612 Software Evolution and Maintenance 3 0 3 7.5
    EEE 501 Applied Digital Image Processing 3 0 3 7.5
    EEE 502 Pattern Recognition 3 0 3 7.5
    EEE 504 Bankaları / Wavelets and Filter Banks 3 0 3 7.5
    EEE 505 Biomedical Signals and Instrumentations 3 0 3 7.5
    EEE 506 Adaptive Signal Processing 3 0 3 7.5
    EEE 509 Selected Topics in Signal Processing 3 0 3 7.5
    EEE 511 Artificial Neural Networks for Signal Processing and Control 3 0 3 7.5
    EEE 512 Optimal Control 3 0 3 7.5
    EEE 522 RF ID systems 3 0 3 7.5
    EEE 527 Principles of Autonomous Vehicle Design 3 0 3 7.5
    EEE 533 Digital VLSI Design 3 0 3 7.5
    EEE 542 Detection and Estimation Theory 3 0 3 7.5
    EEE 543 Basics of Wireless Communications 3 0 3 7.5
    EEE 551 Linear Systems Theory 3 0 3 7.5
    EEE 561 Microprocessor Systems 3 0 3 7.5
    EEE 562 Real-Time Signal Processing 3 0 3 7.5
    EEE 601 Fast Filtering Algorithms 3 0 3 7.5
    EEE 602 Video Processing 3 0 3 7.5
    EEE 612 Chaos and Fractals 3 0 3 7.5
    EEE 652 Stochastic Processes 3 0 3 7.5
    FM 551 Scientific Computation and Simulation in Finance 3 0 3 7.5
    IE 501 Mathematics for Operations Research 3 0 3 7.5
    IE 502 Probabilistic Systems Analysis 3 0 3 7.5
    IE 508 System Simulation and Modeling 3 0 3 7.5
    IE 509 Heuristics 3 0 3 7.5
    IE 510 Discrete Optimization 3 0 3 7.5
    IE 513 Mathematical Programming and Applications 3 0 3 7.5
    IE 515 Multi Objective Optimization 3 0 3 7.5
    IE 520 Constraint Programming 3 0 3 7.5
    IE 530 Evolutionary Algorithms 3 0 3 7.5
    IE 534 Nonlinear Programming 3 0 3 7.5
    IE 535 Advanced Network Optimization 3 0 3 7.5
    IE 540 Applied Stochastic Processes 3 0 3 7.5
    IE 544 Experimental Design in Engineering 3 0 3 7.5
    MATH 504 Statistics 3 0 3 7.5
    MATH 553 Optimization 3 0 3 7.5
    MATH 554 Basic Topics in Mathematics 3 0 3 7.5
    MATH 600 Mathematics Softwares and Research Methods 3 0 3 7.5
    MATH 601 Differential Equations 3 0 3 7.5
    MATH 602 Advanced Linear Algebra and Optimization 3 0 3 7.5
    MATH 654 Discrete Optimization and Heuristic Methods 3 0 3 7.5
    MATH 658 Advanced Data Analysis 3 0 3 7.5
    MATH 659 Graph Theory 3 0 3 7.5
    MATH 662 Cryptography 3 0 3 7.5
    MATH 663 Biomathematics 3 0 3 7.5
    MATH 667 Theory of Finite Elements 3 0 3 7.5
    MATH 668 Spectral Analysis of Differential Operators 3 0 3 7.5
    MATH 671 Fuzzy Optimization 3 0 3 7.5
    STAT 503 Probability Theory 3 0 3 7.5
    STAT 505 Applied Statistical Analysis 3 0 3 7.5
    STAT 558 Design of Experiment 3 0 3 7.5
    STAT 561 Statistical Softwares and Simulation 3 0 3 7.5
    STAT 562 Combinatorial Analysis and Discrete Distributions 3 0 3 7.5
    Additional Notes

    EXPLANATIONS

    As stated in article thirty four of Graduate Degree Education Bylaw

    - Non-thesis Master’s Program is made up of at least ten courses which 
    total up to not less than thirty IUE credits, and one non-credited term project, which make a total of sixty ECTS credits. Semester project does not have IUE credits, and is graded as satisfactory or unsatisfactory.

    - A student may take maximum of three undergraduate courses with the 
    decision of the Graduate School Administrative Board on condition that they have not been taken during their undergraduate education.

    - The student needs to register to a term project during the time when 
    semester projects are taken, and submit a written report at the end.

    - Students must take four of their elective courses from CE codded courses.

    If you need support for these courses due to your disability, please refer to Disability Support Unit. Contact; engelsiz@ieu.edu.tr

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