CANKIRI KARATEKIN UNIVERSITY Bologna Information System


  • Course Information
  • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Artificial Intelligence and Its Applications OSD122 FALL-SPRING 2+0 University E 3
    Learning Outcomes
    1-Interprets the basic concepts and methods of Artificial Intelligence for different fields of study.
    2-Analyzes the Fuzzy Logic theorem and its applications for different fields of study.
    3-Apply Basic Artificial Intelligence Methods and Fuzzy Logic theorem in the related projects for different fields of study.
  • ECTS / WORKLOAD
  • ActivityPercentage

    (100)

    NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14228
    Classroom study (Pre-study, practice)14228
    Assignments0000
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)30188
    Project30188
    Laboratory 0000
    Final exam (exam + preparation) 4011010
    0000
    Total Workload (hours)   82
    Total Workload (hours) / 30 (s)     2,73 ---- (3)
    ECTS Credit   3
  • Course Content
  • Week Topics Study Metarials
    1 Introduction, Turing test, a brief history of AI R1-Chapter-1
    2 Knowledge acquisition, knowledge representation, inferencing R1-Chapter-3
    3 Expert Systems R1-Chapter-5
    4 Expert Systems and its applications for different application fields-I R1-Chapter-5
    5 Expert Systems and its applications for different application fields-II R1-Chapter-5
    6 Hybrid Neural Network and Expert Systems for different application fields-I R1-Chapter-6
    7 Hybrid Neural Network and Expert Systems for different application fields-II R1-Chapter-6
    8 Fuzzy Logic Theorem R2-Chapter-1
    9 Fuzzy Logic and its applications-I (ANFIS) for different application fields R2-Chapter-4
    10 Fuzzy Logic and its applications-II (ANFIS) for different application fields R2-Chapter-4
    11 Genetic Algorithms R3-Chapter-1
    12 Genetic Algorithms and its applications for different application fields R3-Chapter-4
    13 Project Presentations-I
    14 Project Presentations-II
    Prerequisites -
    Language of Instruction Turkish
    Responsible Assist. Prof. Dr. Seda Şahin
    Instructors -
    Assistants -
    Resources R1-Russell, S. & Norvig, P. (1995). Artificial Intelligence A Modern Approach, Prentice-Hall, Inc., R2-Timothy, J.R. (2010). Fuzzy Logic with Engineering Applications, Third Edition, John Wiley & Sons, Ltd. ISBN: 978-0-470-74376-8. R3-Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley.
    Supplementary Book -
    Goals The main aim of this course is to construct automated models to solve real world problems after learning theoretical background of the basic AI Methods and Fuzzy Logic Theorem for different application fields.
    Content Introduction, Turing test, a brief history of AI, Knowledge acquisition, knowledge representation, inferencing, Expert Systems, Expert Systems and its applications for different application fields-I, Expert Systems and its applications for different application fields-II, Hybrid Neural Network and Expert Systems for different application fields-I, Hybrid Neural Network and Expert Systems for different application fields-II, Fuzzy Logic Theorem, Fuzzy Logic and its applications for different application fields-I (ANFIS), Fuzzy Logic and its applications for different application fields-II (ANFIS), Genetic Algorithms, Genetic Algorithms and its applications for different application fields.
  • Program Learning Outcomes
  • Program Learning Outcomes Level of Contribution
    1 Develops effective and empathic communication skills -
    2 Promotes the universality of social and cultural rights -
    3 Evaluates issues with analytical and critical thinking -
    4 Establishes effective oral and written communication -
    5 Uses information technologies effectively and in accordance with ethical rules -
    6 Develops a positive attitude towards lifelong learning -
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