Information Package / Course Catalogue
Artificial Intelligence Fundamentals
Course Code: RYZ105
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

The aim of this course is to introduce students to the basic concepts of artificial intelligence, explain its historical development, show the application areas of artificial intelligence in daily life and industry, provide general information about basic algorithms and techniques, and raise awareness about ethical/social impacts. It is also aimed for students to develop a basic understanding of how artificial intelligence systems work and to reach the competence to follow artificial intelligence-based technologies in relevant professional fields.

Course Content

It includes the definition of artificial intelligence, its history, sub-fields of artificial intelligence, basic artificial intelligence solutions, artificial intelligence and ethics, and the concepts of artificial intelligence in the future.

Name of Lecturer(s)
Ins. Merve MUTİ İSTEK
Learning Outcomes
1.Be able to explain the basic definition and sub-fields of artificial intelligence
2.Summarize the history and development of artificial intelligence
3.Being able to recognize basic approaches such as machine learning, artificial neural networks, expert systems
4.Being able to give examples of artificial intelligence applications in daily life
5.Developing awareness about the ethical and social impacts of artificial intelligence
6.Being able to realize the potential of artificial intelligence in professional fields
Recommended or Required Reading
1.Course notes
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Artificial Intelligence: Definition and History
Week 2 - Theoretical
Development of Artificial Intelligence and Its Milestones
Week 3 - Theoretical
Basic Concepts of Artificial Intelligence
Week 4 - Theoretical
Machine Learning (ML)
Week 5 - Theoretical
Machine Learning (ML)
Week 6 - Theoretical
Deep Learning (DL)
Week 7 - Theoretical
Artificial Neural Networks
Week 8 - Theoretical
Natural Language Processing
Week 9 - Theoretical
Application Areas: Healthcare, Automotive, Education, Finance
Week 10 - Theoretical
Image Processing and Computer Vision
Week 11 - Theoretical
Artificial Intelligence in Robotics and Autonomous Systems
Week 12 - Theoretical
Artificial Intelligence Ethics and Social Impacts
Week 13 - Theoretical
The Future of Artificial Intelligence: Opportunities and Risks
Week 14 - Theoretical
General Evaluation, Sample Applications
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Individual Work140228
Midterm Examination1011
Final Examination1011
TOTAL WORKLOAD (hours)72
Contribution of Learning Outcomes to Programme Outcomes
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OÇ-1
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OÇ-6
5
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5
Adnan Menderes University - Information Package / Course Catalogue
2026