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  • Course Information
    • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Introduction to Artificial Intelligence EBM531 FALL-SPRING 3+0 Faculty E 6
    Learning Outcomes
    1-Applies the basic concepts of Artificial Intelligence.
    2-Explains basic Artificial Intelligence Methods.
    3-Explains the Fuzzy Logic theorem and its applications.
    4-Develops Basic Artificial Intelligence Methods and Fuzzy Logic theorem projects in different fields of study.
    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. (4th ed.). Prentice-Hall Inc. R2- Timothy, J.R.(2010). Fuzzy Logic with Engineering Applications. (3rd ed.). John Wiley and Sons Ltd. R3- Goldberg, D.E. (1989). Genetic Algorithms in Search Optimization and Machine Learning. (13th ed.). 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.
    Content Introduction, Turing test, a brief history of AI, Knowledge acquisition, knowledge representation, inferencing, Expert Systems, Expert Systems and its applications-I, Expert Systems and its applications-II, Hybrid Neural Network and Expert Systems-I, Hybrid Neural Network and Expert Systems-II, Fuzzy Logic Theorem, Fuzzy Logic and its applications-I (ANFIS), Fuzzy Logic and its applications-II (ANFIS), Genetic Algorithms, Genetic Algorithms and its applications.
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