Course Details

MACHINE LEARNING

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
5	BENG418	MACHINE LEARNING	3+0+0	3	5

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	BIOENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	The course presents an introduction to basic machine learning approaches.
Course Content	The main topics include: Supervised learning (support vector machines, decision tree, random forest), Unsupervised learning (hierarchical clustering, k-means clustering, dimensionality reduction). Also, the course will include numerous case studies and applications from various areas
Course Methods and Techniques	The course is taught with a student-centered approach. The course includes in-class activities, group work, assignments, mini projects. Various interactive methods such as discussions, peer interaction and hands-on activities will be used.
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Associate Prof.Dr. M. Duygu SAÇAR DEMİRCİ https://avesis.agu.edu.tr/duygu.sacar duygu.sacar@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Nilsson N.J. (1999) Introduction to machine learning
	Lecture notes and reading materials will be shared weekly through the CANVAS platform.
	Lecture notes and reading materials will be shared weekly through the CANVAS platform. The main textbook is: Ernesto Iadanza. (2019). Clinical Engineering Handbook. Academic Press.
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Course Category

Mathematics and Basic Sciences	%10
Engineering	%25
Engineering Design	%35
Social Sciences	%0
Education	%5
Science	%5
Health	%5
Field	%60

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	% 5
Quiz/Küçük Sınav	1	% 10
Ödev	3	% 20
Proje/Çizim	1	% 30
Final examination	1	% 35
Total	7	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Araştırma Ödevi	5	3	15
Tartışma	12	3	36
Yazılı Sınav	1	3	3
Ev Ödevi	1	5	5
Sınıf İçi Aktivitesi	9	3	27
Sunum	1	1	1
Proje	1	10	10
Araştırma	3	2	6
Takım/Grup Çalışması	1	1	1
Yüz Yüze Ders	14	3	42
Final Sınavı	1	4	4
Total Work Load			Number of ECTS Credits 5 150

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

No	Learning Outcomes
1	Describe the main elements of machine learning methods
2	Examine the basic differences between methods
3	Choose appropriate design to build machine learning workflows
4	Design a machine learning workflow to solve a real problem

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to the machine learning
2	Introduction to the machine learning
3	Classification
4	Classification
5	Classification
6	Classification
7	Classification
8	Classification
9	Classification
10	Clustering and Student Projects
11	Clustering and Student Projects
12	Clustering and Student Projects
13	Clustering and Student Projects
14	Clustering and Student Projects

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=72052&lang=en