Information Package / Course Catalogue
Robotics and Artificial Intelligence Applications
Course Code: RYZ102
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

The aim of the course is to teach the working principles of robots, the basic components of robotic systems and the application methods of robotic applications to real-world problems together with artificial intelligence (AI); to provide the skills of data analysis, learning, decision making and developing automatic systems by using artificial intelligence algorithms in robotic systems. This course aims to teach how various artificial intelligence techniques can be used in various industrial, medical and daily life applications of robots within the scope of basic topics such as robot design, control systems, kinematics, dynamics and sensor technologies, by providing practical application skills. It also aims to teach the use, programming and control of sub-disciplines such as machine learning, deep learning, natural language processing (NLP) and computer vision on robotic systems.

Course Content

The robotics and artificial intelligence applications course provides students with a comprehensive understanding of the applications of robotic technologies in different areas based on artificial intelligence and the process from the design to the use of these technologies. It is a course that provides the skills to analyze the functioning of today's technology, adapt artificial intelligence technologies to robotic systems, use the technological devices used in the most efficient and comprehensive way, and make the right additions by knowing the working principles of artificial intelligence-based robotic systems.

Name of Lecturer(s)
Lec. İsmail MERSİNKAYA
Learning Outcomes
1.They understand the basic components and operation of robotic systems, grasp the role of sensors and actuators, and learn to design programmable systems with microcontrollers.
2.Designs robotic systems for Robotics and Artificial Intelligence applications, produces prototypes and uses these systems to solve real-world problems.
3.In Robotics and Artificial Intelligence applications, they design and implement basic robotic algorithms such as motion control of robots, processing of sensor data and decision algorithms.
4.They will have basic theoretical and application knowledge for the development of robotics and artificial intelligence applications in agriculture, industry, assistant robots and healthcare fields, as well as drones and autonomous systems.
5.They understand their ethics, safety and social responsibilities in robotics and AI applications and become aware of human interaction and collaboration with robots.
Recommended or Required Reading
1.Robotik: Temel Kavramlar ve Uygulamalar - Haluk Eren
2.Yapay Zeka: Temelleri ve Uygulamaları - Abdullah Tasçı
3.Artificial Intelligence: A Modern Approach - Stuart Russell, Peter Norvig
Weekly Detailed Course Contents
Week 1 - Theoretical
Robotic System Components
Week 2 - Theoretical
Robotics Programming
Week 3 - Theoretical
Robotics Programming
Week 4 - Theoretical
Robotics Programming
Week 5 - Theoretical
Industrial and Agricultural Robots Artificial Intelligence Applications
Week 6 - Theoretical
Industrial and Agricultural Robots Artificial Intelligence Applications
Week 7 - Theoretical
Medical Robots and Artificial Intelligence Application Applications in Healthcare
Week 8 - Theoretical
Medical Robots and Artificial Intelligence Application Applications in Healthcare
Week 9 - Theoretical
Artificial Intelligence Applications for Drones and Autonomous Systems
Week 10 - Theoretical
Artificial Intelligence Applications for Drones and Autonomous Systems
Week 11 - Theoretical
Data Analysis and Big Data Processing
Week 12 - Theoretical
Robots and Artificial Intelligence Applications
Week 13 - Theoretical
Robots and Artificial Intelligence Applications
Week 14 - Theoretical
Robots and Artificial Intelligence Applications
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Project100220
Individual Work140114
Midterm Examination1011
Final Examination1011
TOTAL WORKLOAD (hours)78
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
OÇ-1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
OÇ-2
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
OÇ-3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
OÇ-4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
OÇ-5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026