Information Package / Course Catalogue
Introduction to Machine Learning
Course Code: RYZ107
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

To provide information about the fundamental concepts, methods, and application areas of machine learning. To enable students to use machine learning methods in data analysis, classification, and prediction problems.

Course Content

This course introduces machine learning and explores the relationship between artificial intelligence and data. It examines both supervised and unsupervised learning approaches used in machine learning. Furthermore, it investigates the applications of machine learning in robotics, automation, and industrial settings.

Name of Lecturer(s)
Learning Outcomes
1.Ability to explain the fundamental concepts and terminology of machine learning.
2.Ability to distinguish between supervised and unsupervised learning methods.
3.Ability to explain the working principles of basic machine learning programs.
4.The ability to select and interpret appropriate machine learning methods on simple data packets.
5.The ability of machine learning to expand its application areas across different sectors.
Recommended or Required Reading
1.Ethem Alpaydın, Machine Learning, Boğaziçi University Press.
2.Tom M. Mitchell, Machine Learning, McGraw-Hill Education.
3.Sinan Uğuz, Theoretical Aspects of Machine Learning and Python Applications: A School of Thought in Artificial Intelligence, Nobel Publishing.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Machine Learning
Week 2 - Theoretical
The Relationship Between Artificial Intelligence and Machine Learning
Week 3 - Theoretical
The Concept of Data in Machine Learning
Week 4 - Theoretical
Data Preprocessing Techniques
Week 5 - Theoretical
Supervised Learning
Week 6 - Theoretical
Regression Algorithms
Week 7 - Theoretical
Classification Algorithms
Week 8 - Theoretical
Decision Trees and Random Forests
Week 9 - Theoretical
Support Vector Machines (SVM)
Week 10 - Theoretical
k-Nearest Neighbor (k-NN) Algorithm
Week 11 - Theoretical
Unsupervised Learning and Clustering Algorithms
Week 12 - Theoretical
Introduction to Artificial Neural Networks
Week 13 - Theoretical
Deep Learning and Current Applications
Week 14 - Theoretical
Machine Learning Applications
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures10%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Individual Work100220
Midterm Examination1011
Final Examination1011
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
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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