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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    EEE 652 | Course Introduction and Application Information

    Course Name
    Stochastic Processes
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    EEE 652
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Third 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 to study basic probability concepts, time averages, statistical averages, random variables, moments, CDF, PDF, and PSD functions, random vectors, stationary and ergodic random processes and random signals
    Learning Outcomes

    The students who succeeded in this course;

    • be able to determine the probability density function and calculate probability,
    • be able to identify the parameters of discrete and continuous random variables,
    • be able to determine the characteristics of the sum of the random variables,
    • be able to do hyphothesis testing based on the random observations,
    • be able to determine the autocorrelation and power spectral density of random signals,
    • be able to obtain the best estimate based on the random measurements,
    • be able to determine the Markov Chain model of random processes,
    Course Description This is a mandatory course in Electrical and Electronics Engineering Ph.D. Program. The course is in the area of Random Signals, Probability Theory, Stochastic Processes and Random Signal Processing. The course aims to study basic probability concepts, time averages, statistical averages, random variables, moments, CDF, PDF, and PSD functions, random vectors, stationary and ergodic random processes and random signals

     



    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 Review of signals and systems Textbooks Ch 1
    2 Time averages, correlation, covariance Textbooks Ch 7
    3 Processing of random signals by LTI systems Textbooks Ch 7
    4 Applications: Estimation problem Textbooks Ch 7
    5 Probability theory Textbooks Ch 2
    6 Random variable concept Textbooks Ch 3
    7 Cumulative Distribution and Probability Density Functions Textbooks Ch 3
    8 Statistical averages, mean, variance, higher order moments Textbooks Ch 3
    9 Random vectors Textbooks Ch 4
    10 Functions of random variables Textbooks Ch 5
    11 Characteristic function of random variables Textbooks Ch 5
    12 Stochastic processes Textbooks Ch 6
    13 Ergodic and stationary processes Textbooks Ch 6
    14 Applications in Electrical and Electronics Engineering Textbooks Ch 7
    15 Review of the course and problem session
    16 Final

     

    Course Notes/Textbooks

    Alberto Leon-Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3/E, Pearson, 2008. ISBN: 9780131471221
    Suggested Readings/Materials

    Stark and Woods, Probability and Random Processes with Applications to Signal Processing, 4/E Pearson, 2012 ISBN: 9780273752288

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    6
    30
    Presentation / Jury
    Project
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    30
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    7
    60
    Weighting of End-of-Semester Activities on the Final Grade
    1
    40
    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
    0
    Portfolio
    0
    Homework / Assignments
    5
    11
    55
    Presentation / Jury
    0
    Project
    0
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    20
    20
    Final Exam
    1
    32
    32
        Total
    225

     

    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 Computer Engineering, evaluates, interprets and applies information.
    -
    -
    -
    X
    -
    2 Is well-informed about contemporary techniques and methods used in Computer 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 Computer 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 Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer 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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