Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Vocational School Of Information Technologies
  • Department of Electronics and Automation
  • Robotics and Artificial Intelligence Program
  • Course Structure Diagram with Credits
  • Artificial Intelligence
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title RYZ106 - Artificial Intelligence
Course Type Required Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 4.0
Course Instructor(s) ÖĞRETİM GÖREVLİSİ ÖZGÜR ÖZŞEN
Mode of Delivery Distance Learning
Prerequisites
Courses Recomended
Recommended Reading List
Assessment methods and criteria
Work Placement
Sustainability Development Goals Quality Education , Decent Work and Economic Growth , Industry, Innovation and Infrastructure , Sustainable Cities and Communities , Responsible Production and Consumption

Content

Weeks Topics
Week - 1 Definition and history of the concept of artificial intelligence
Week - 2 Basic concepts of artificial intelligence: intelligence, intelligent behavior, types of artificial intelligence
Week - 3 The importance of artificial intelligence applications in daily life and industry
Week - 4 Machine learning fundamentals, Supervised and unsupervised learning
Week - 5 Machine learning algorithms: decision trees, K-nearest neighbors, support vector machines
Week - 6 Deep learning and artificial neural networks, Convolutional neural networks and fully connected neural networks
Week - 7 Deep learning applications: image classification, natural language processing
Week - 8 Natural language processing and text classification
Week - 9 Image processing and recognition
Week - 10 Artificial intelligence application areas: healthcare, automotive, finance, education
Week - 11 Artificial intelligence ethical principles and concerns, Data-driven decision making and ethical issues. Societal impacts and responsibilities of artificial intelligence technologies
Week - 12 Security threats and vulnerabilities of artificial intelligence systems, Data privacy and security measures related to artificial intelligence, Artificial intelligence security solutions and applications
Week - 13 Students develop projects using artificial intelligence techniques.
Week - 14 Project presentations and evaluations

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Demonstration
  • Drill - Practise
  • Problem Solving
  • Brain Storming
  • Proje Design/Management
  • Competences
  • Productive
  • Rational
  • Entrepreneur
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Information Management
  • Elementary computing skills
  • Decision making
  • Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100
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