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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    CE 533 | Course Introduction and Application Information

    Course Name
    Artificial Intelligence
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    CE 533
    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 Problem Solving
    Q&A
    Critical feedback
    Lecture / Presentation
    National Occupation Classification -
    Course Coordinator
    Course Lecturer(s)
    Assistant(s) -
    Course Objectives Artificial Intelligence (AI) is devoted to the computational study of intelligent behavior. The element that the fields of AI have in common is the creation of agents/machines that can "think". This course will cover a broad technical introduction to the techniques that enable agents/computers to behave intelligently: problem solving, representing knowledge, reasoning, learning, perceiving, and interpreting. The bulk of this course reflects this diversity. We will examine the fundamental questions and issues of AI and will explore the essential techniques. In the special topics, several AI applications will be presented.
    Learning Outcomes

    The students who succeeded in this course;

    • will be able to discuss a broad range of issues in the field of AI.
    • will be able to use and discuss the basic techniques of the field.
    • will be able to evaluate potential applications of AI technology.
    • will be able to identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
    • will be able to implement basic AI algorithms (e.g., standard search algorithms).
    Course Description

     



    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 Chapter 1
    2 Intelligent Agents Chapter 2
    3 Solving Problems by Searching Chapter 3
    4 Local Search Chapter 4
    5 Adversarial Search Chapter 5
    6 Constraint Satisfaction Problems Chapter 6
    7 Logical Agents, Propositional Logic Chapter 7
    8 First-Order Logic Chapter 8
    9 Inference in FOL Chapter 9
    10 Classical Planning Chapter 10
    11 Uncertainty Chapter 13 & 14
    12 Learning Chapter 18
    13 Reinforcement Learning Chapter 21
    14 Reinforcement Learning Chapter 21
    15 Paper Presentations
    16 Final Review

     

    Course Notes/Textbooks

    S.Russell, P.Norvig, Artificial Intelligence:  A Modern Approach, 3rd Edition, Prentice Hall, 2010
    Suggested Readings/Materials

    Nick Bostrom, Superintelligence: Paths, Dangers, Strategies

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    3
    15
    Presentation / Jury
    1
    25
    Project
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    20
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    5
    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
    15
    4
    60
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    3
    10
    30
    Presentation / Jury
    1
    12
    12
    Project
    0
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    35
    35
    Final Exam
    1
    40
    40
        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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