CANKIRI KARATEKIN UNIVERSITY Bologna Information System


  • Course Information
  • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Artificial Neural Networks EEM592 FALL-SPRING 3+0 E 6
    Learning Outcomes
    1-Defines artificial neural networks
    2-Classifies artificial neural networks
    3-Uses artificial neural networks to solve various problems
  • ECTS / WORKLOAD
  • ActivityPercentage

    (100)

    NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14684
    Assignments20155
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)3012020
    Project0000
    Laboratory 0000
    Final exam (exam + preparation) 5012525
    0000
    Total Workload (hours)   176
    Total Workload (hours) / 30 (s)     5,87 ---- (6)
    ECTS Credit   6
  • Course Content
  • Week Topics Study Metarials
    1 General description of artificial neural networks (ANN) R1-Chapter-1
    2 General characteristics and basics of ANN R1-Chapter-2
    3 Important disadvantages and history of ANN R1-Chapter-3
    4 Biological nerve cells and artificial nerve cells R1-Chapter-4
    5 Learning and testing in YSA R1-Chapter-5
    6 Most used models in ANNs R1-Chapter-6
    7 Single-layer networks R1-Chapter-7
    8 Multi-layer networks R1-Chapter-8
    9 Forward and backward calculation in Multi-layer networks R1-Chapter-9
    10 Measurement of performance in Multi-layer networks R1-Chapter-10
    11 LVQ network model and features-I R1-Chapter-11
    12 LVQ network model and features-II R1-Chapter-11
    13 Adaptive Resonance Theory (ART) networks model R1-Chapter-12
    14 Elman network model R1-Chapter-13
    Prerequisites -
    Language of Instruction Turkish
    Responsible Asst. Prof. Dr. Zafer Civelek
    Instructors

    1-)Doktor Öğretim Üyesi Zafer Civelek

    Assistants -
    Resources R1-Öztemel, E. (2006). Yapay Sinir Ağları, Papatya yayınları, İstanbul.
    Supplementary Book -
    Goals To give comprehensive information about artificial neural networks
    Content General features and basic tasks of ANN, Single-layer sensor networks, Multi-layer sensor (MGA) model, LVQ network model and features, Adaptive Resonance Theory (ART) networks model, Elman network model
  • Program Learning Outcomes
  • Program Learning Outcomes Level of Contribution
    1 Acquires information by carrying out scientific research in the field of Electrical and Electronics Engineering, evaluates the findings and makes comments 5
    2 Complements the restricted or incomplete information and applies it, unifies the multidisciplinary information -
    3 Designs and implements a system meeting the requirements in the field of Electrical and Electronics Engineering -
    4 Makes an interpretation of a problem in the field of Electrical and Electronics Engineering, develops models for solutions and applies innovative methods in these solutions 2
    5 Has comprehensive knowledge on the contemporary applied method and techniques used in the field of Electrical and Electronics Engineering and their limitations -
    6 Undertakes and implements analytic, simulation or experimental types of research and has the ability to solve the complex problems encountered there 3
    7 Can participate and assume responsibility in multidisciplinary task forces -
    8 Observes the scientific, professional and ethical rules during data collection, its introduction and interpretation -
    9 Be aware of recent advances and developments in the field of Electrical and Electronics Engineering, learns, analyses and applies them wherever needed 4
    10 Publishes his/her research findings verbally and in written forms in national and international arena -
    Çankırı Karatekin Üniversitesi  Bilgi İşlem Daire Başkanlığı  @   2017 - Webmaster