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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    EEE 502 | Course Introduction and Application Information

    Course Name
    Pattern Recognition
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    EEE 502
    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 This course covers foundations of pattern recognition algorithms and their applications. Topics covered include statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering.
    Learning Outcomes

    The students who succeeded in this course;

    • learn to use and discuss the basic techniques/algorithms of the field,
    • have knowledge of the advantages and limitations of different pattern recognition algorithms,
    • be able to evaluate potential applications of pattern recognition techniques,
    Course Description Pattern recognition algorithms and their applications, statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering.

     



    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 Introduction to Pattern Recognition Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 1)
    2 Bayesian Decision Theory Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 2)
    3 Maximum Likelihood and Bayesian Estimation Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 3)
    4 Nonparametric Techniques Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 4)
    5 Linear Discriminant Functions Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 5)
    6 Feature Selection and Dimension Reduction Techniques Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 5)
    7 Multilayer Neural Networks Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6)
    8 Midterm
    9 Multilayer Neural Networks Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6)
    10 Radial Basis Function Networks Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6)
    11 Stochastic Methods Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 7)
    12 Nonmetric Methods Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 8)
    13 Unsupervised Learning and Clustering Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 10)
    14 In-class Presentations
    15 In-class Presentations
    16 Review of the Semester  

     

    Course Notes/Textbooks The textbook referenced above and course slides
    Suggested Readings/Materials Related 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 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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