Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
7ME484APPLIED ARTIFICIAL INTELLGENCE (AL) IN MECHANICAL SYSTEMS2+1+03515.05.2026

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program MECHANICAL ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Explaining the foundations of artificial intelligence and machine learning. Implementing AI-driven solutions to address challenges in energy efficiency and biomedical engineering. Integrating machine learning techniques to develop and enhance autonomous system applications. Assessing AI methodologies to solve complex problems in mechanical engineering.
Course Content This course offers a solid foundation in the applications of artificial intelligence relevant to mechanical engineering, preparing students to effectively lead and contribute to the integration of AI technologies within the discipline. From advancing autonomous systems to tackling challenges in energy and biomedical engineering, this course examines cutting-edge artificial intelligence methodologies tailored to the specific contexts of mechanical engineering. Students will utilize artificial intelligence tools and methodologies to address practical challenges in the field, including the enhancement of design innovation, the improvement of energy efficiency, and the advancement of biomedical engineering applications. Students will further develop their AI competencies through hands-on experience, engaging in individual projects and exercises that reinforce theoretical knowledge within practical, real-world contexts.
Course Methods and Techniques In this course, student-centred and teacher-centred methods of instructions will be used together. The basic principles of various separation techniques will be covered in the lectures.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Instructor Dr. Gökhan Ateş
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning
Sutton, R. S. and Barto, A. G., Reinforcement Learning: An Introduction
Course Notes The course syllabus, course materials including lecture notes, links to related websites, assignments, articles will be accessed on CANVAS.
Documents Dokumanlar ders notları ve ilgili kaynak paylasimi seklinde sağlanmaktadır
Assignments Arastirma ödevi sunum becerileri ile birleştirilir
Exams Quiz ve final ödeviyle dersin öğrenme ciktilari degerlendirilir

Course Category
Mathematics and Basic Sciences %10
Engineering %90

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı 1 % 30
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı 1 % 35
Quiz/Küçük Sınav 3 % 25
Ödev 1 % 10
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Araştırma Ödevi 1 3 3
Yazılı Sınav 1 2 2
Grup Sunumu 1 3 3
Grup Projesi 1 72 72
Ev Ödevi 1 3 3
Sınıf İçi Aktivitesi 6 1 6
Kısa Sınav 3 1 3
Araştırma 1 3 3
Takım/Grup Çalışması 1 18 18
Yüz Yüze Ders 14 3 42
Derse Devam 1 1 1
Total Work Load   Number of ECTS Credits 5 156

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Describe fundamentals of artificial intelligence and machine learning.
2 Discuss the role of artificial intelligence in autonomous systems through the implementation of essential algorithms, including reinforcement learning and object detection methods.
3 Assess motion planning, perception, learning, state estimation, localization, and visual perception in robotics and self-driving cars through key algorithms like object detection.
4 Develop an understanding of AI applications in biomedical sciences energy problems.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Artificial Intelligence in Mechanical Engineering Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2 Fundamentals of Machine Learning Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
3 Supervised Learning: Regression Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
4 Supervised Learning: Classification Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
5 Model Evaluation and Overfitting Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
6 Unsupervised Learning Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
7 Introduction to Neural Networks Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning
8 Deep Learning Fundamentals Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning
9 AI Applications in Mechanical Design and Optimization Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning
10 AI Applications in Energy Systems
11 AI Applications in Biomedical Engineering Selected journal articles and case studies related to AI applications in mechanical engineering.
12 AI in Manufacturing and Fault Detection Selected journal articles and case studies related to AI applications in mechanical engineering.
13 Reinforcement Learning Sutton, R. S. and Barto, A. G., Reinforcement Learning: An Introduction
14 Reinforcement Learning for Mechanical and Autonomous Systems Selected journal articles and case studies related to AI applications in mechanical engineering.

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
All 5 5 4 4 4
C1 5 5 4 4 4
C2 5 4 4 3 3
C3 5 4 4 3 4
C4 4 4 3 5 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=79527&lang=en
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