Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
5	EE3005	BIOMEDICAL SYSTEM DESIGN CAPSULE	6+3+0	8	10	14.08.2025

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	ELECTRICAL-ELECTRONICS ENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	(1) to introduce the biomedical system design process. (2) to help students develop biotechnology based idea generation and entrepreneurship skills. (3) to help students understand biosignal/bioimage processing concepts via an applied project and online resources. (4) to help students to understand machine learning concepts in the biomedical engineering field via an applied project and online resources. (5) to improve competencies on innovation, project management and teamwork. (6) to supervise team-based system design projects.
Course Content	This course introduces the fundamentals of biomedical system design. The course covers the following main topics: Biomedical system design problem definition, design principles, idea generation, ethics, research methodology, entrepreneurship, machine learning and biomedical signal/image processing.
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	Research Assist. DR. MAHMUT BÜYÜKBAŞ mahmut.buyukbas@agu.edu.tr
Name of Lecturers	Research Assist.Dr. Mahmut Büyükbaş mahmut.buyukbas@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources

Ethem Alpaydın, Introduction to Machine Learning, 4th ed., MIT Press, 2020.
Digital Image Processing, 3rd ed, by Gonzalez and Woods

Course Category

Mathematics and Basic Sciences	%20
Engineering	%20
Engineering Design	%20
Field	%40

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
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı	1	% 25
Quiz/Küçük Sınav	5	% 30
Ödev	5	% 25
Final examination	1	% 10
Uygulama Çalışmaları (Laboratuar,Sanal Mahkeme,Stüdyo Çalışmaları vb.)	4	% 10
Total	16	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Belirsiz	11	2	22
Yazılı Sınav	3	1	3
Deney	4	3	12
F2F Dersi	1	2	2
Grup Sunumu	2	3	6
Grup Projesi	2	30	60
Ev Ödevi	5	5	25
Sınıf İçi Aktivitesi	1	1	1
Sunum için Hazırlık	2	6	12
Sunum	1	3	3
Proje	1	5	5
Kısa Sınav	1	1	1
Rapor	1	4	4
Araştırma	3	5	15
Simülasyon	5	5	25
Senkron Ders	1	2	2
Ders dışı çalışma	2	5	10
Öğretici Sunum/Açıklama	1	3	3
Yüz Yüze Ders	14	6	84
Asenkron Ders	1	2	2
Final Sınavı	1	3	3
Total Work Load			Number of ECTS Credits 10 300

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Develop a project idea in the biomedical engineering field that includes machine learning and biosignal/bioimage processing with the help of patent and market research.
2	Explain principles of a biomedical system design and commercialization.
3	Design and implement a biomedical system that includes biosignal/bioimage processing and machine learning concepts.
4	Develop self-learning skills by following courses that are available on online learning platforms such as Coursera, edX, etc.
5	Present ideas and learning process outcomes in written and oral form.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Overview of Digital Image Processing applications Introduction to Machine Learning
2	Image sampling and quantization, Relation of the pixels Linear Regression with One Variable
3	Intensity transformations, histogram processing, spatial filters Linear Regression with Multiple Variables
4	Fourier transform of sampled functions, Discrete Fourier Transform (DFT) and properties of 2D DF Logistic Regression
5	Filtering in frequency domain Regularization
6	Filtering in frequency domain (Continued) Neural Networks
7	Image restoration and reconstruction Machine Learning System Design
8	Image restoration and reconstruction (Continued) Support Vector Machines
9	Morphological operations Unsupervised Learning
10	Morphological operations (Continued) Dimensionality Reduction
11	Good project management practices
12	Group Based Project Supervision: Feedbacks and Troubleshooting
13	Group Based Project Supervision: Feedbacks and Troubleshooting
14	Group Based Project Supervision: Feedbacks and Troubleshooting
15
16

Contribution of Learning Outcomes to Programme Outcomes

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

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=78411&lang=en