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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (With Thesis)

    IE 590 | Course Introduction and Application Information

    Course Name
    Advanced Topics in IE and OR
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    IE 590
    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 objective of this course is to determine the relative efficiencies of units (decision making units) in the production process or in any sector in the terms of their performance in converting inputs into outputs. The aims of the methodologies in this course are benchmarking, classification, ranking and making projection for these units. At this point, the methods both depent on time/not depent on time and deterministic/stochastic will be investigated.
    Learning Outcomes

    The students who succeeded in this course;

    • At the end of this course the students are expected to be able to • Learn the efficiency analysis methods • Identify the efficiency analysis methods to apply a problem • will be able to propose and advise policies to the institutions and organizations in order to be effective.
    Course Description Definition of efficiency and productivity terms, Introduction to Data Envelopment Analysis (DEA), Presentation of basic DEA models and their applications, Introduction to Stochastic Frontier Analysis (SFA), Presentation of basic SFA model and its applications, Introduction to Malmquist Index (MI), Presentation of MI method and its applications , Introduction to Window Analysis (WA), Presentation of WA method and its applications
    Related Sustainable Development Goals

     



    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
    1 Introduction
    2 Random Samples & Point Estimation
    3 Statistical Intervals for a Single Sample – I
    4 Statistical Intervals for a Single Sample – II
    5 Test of Hypothesis for a Single Sample – I
    6 Test of Hypothesis for a Single Sample – II
    7 Test of Hypothesis for a Single Sample – III
    8 Midterm
    9 Statistical Inference for Two Samples – I
    10 Statistical Inference for Two Samples – II
    11 Statistical Inference for Two Samples – III
    12 Simple Linear Regression
    13 Simple Linear Regression
    14 Multiple Linear Regression Line
    15 Review of the term
    16 Final Exam

     

    Course Notes/Textbooks

    Probability and Statistics in Engineering, W.Hines, D.C. Montgomery, D.M. Goldsman, C.M. Borror, 4th Edition, John Wiley & Sons, Inc.
    Suggested Readings/Materials Instructor notes and lecture slides

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    4
    20
    Portfolio
    Homework / Assignments
    1
    15
    Presentation / Jury
    Project
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    30
    Final Exam
    1
    35
    Total

    Weighting of Semester Activities on the Final Grade
    6
    65
    Weighting of End-of-Semester Activities on the Final Grade
    1
    35
    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
    0
    Study Hours Out of Class
    14
    5
    70
    Field Work
    0
    Quizzes / Studio Critiques
    4
    13
    52
    Portfolio
    0
    Homework / Assignments
    2
    15
    30
    Presentation / Jury
    21
    0
    Project
    0
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    0
    Final Exam
    1
    30
    30
        Total
    230

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    		 Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information.
    -
    -
    -
    -
    -
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    -
    -
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.
    -
    -
    -
    -
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    -
    -
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    -
    -
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs
    -
    -
    -
    -
    -
    7 Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    -
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    -
    -
    -
    11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
    -
    -
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    -
    -
    -
    -

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

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