Course Details

MACHINE-LEARNING

BENG622

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
1BENG622MACHINE-LEARNING3+0+037,5

Course Details
Language of Instruction English
Level of Course Unit Master'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 Explaining the basic concepts of machine learning.
Demonstrating how to design machine learning workflows.
Course Content This course covers the fundamental concepts of machine learning. Implementation of widely used machine learning methods such as Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, k-means clustering and hierarchical clustering on various datasets (biological, medical etc.) will be also examined by using freely available data science platforms (e.g. KNIME, Weka).
Course Methods and Techniques Ders anlatımı öğretim üyesi tarafından yapılacaktır. Konular, sunumlar eşliğinde aktarılacak, ders sırasında karşılıklı soru-cevap, tartışma ve örnek analiz yöntemleriyle öğrenci katılımı sağlanacaktır. Öğrenciler, verilen makaleler ve ders materyalleri üzerinden bireysel okuma ve analiz yapacaklardır.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. DUYGU SAÇAR DEMİRCİ duygu.sacar@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Introduction to Data Mining
To Be Announced weekly via Canvas

Course Category
Mathematics and Basic Sciences %100

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 % 20
Sunum/Seminer 2 % 40
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
F2F Dersi 14 3 42
Sunum için Hazırlık 2 15 30
Sunum 2 2 4
Proje 1 20 20
Okuma 10 4 40
Araştırma 14 5 70
Ders dışı çalışma 10 1 10
Final Sınavı 1 15 15
Total Work Load   Number of ECTS Credits 7,5 231

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Describe the main elements of machine learning methods
2 Explain the basic differences between methods
3 Use KNIME for machine learning
4 Design a machine learning workflow to solve a real problem


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Machine Learning
2 Data
3 Preprocessing
4 Feature Selection
5 Decision Tree
6 Support Vector Machine
7 Fall/Spring Break
8 Random Forests and Ensemble Methods
9 Neural Networks
10 Model Evaluation and Validation
11 k-Means Clustering
12 Hierarchical Clustering and Dendrograms
13 Dimensionality Reduction Techniques
14 Workflow Design in KNIME
15 Presentations
16 Final


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 2 3 3 3 2 5 3 1 4 4 1
C2 5 2 3 3 3 2 5 3 1 4 4 1
C3 5 2 3 3 3 2 5 3 1 4 4 1
C4 5 2 3 3 3 2 5 3 1 4 4 1

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


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=78004&lang=en
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