Information Package / Course Catalogue
Introduction to Machine Learning
Course Code: RYZ104
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

The aim of this course is to teach students the Python programming language and provide introductory-level knowledge in machine learning. It is intended for students to learn the fundamental concepts of machine learning through data analysis, basic algorithms, and simple applications, thereby gaining the ability to develop technological solutions to everyday problems

Course Content

Python programming language, data analysis and visualization processes, introduction to machine learning concepts, basic algorithms (regression, decision trees, classification)

Name of Lecturer(s)
Ins. Neslihan BİLİNMEZ
Learning Outcomes
1.Introduction to Python programming language, basic data types, and control structures
2.Uses basic Python libraries (NumPy, Pandas, Matplotlib) for data analysis and visualization.
3.Defines the fundamental concepts of machine learning and builds sample models.
4.Applies simple classification, regression, and clustering algorithms on real datasets.
5.Implements data preparation, model training, and validation steps in the machine learning process.
Recommended or Required Reading
1.Python Ile Makine Öğrenimi Ve Derin Öğrenme, Ali Şir ATTİLA
2.Python ile Makine Öğrenmesi, Ömer Deperlioğlu, Utku Köse
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Python programming language, basic data types, and control structures
Week 2 - Theoretical
Functions, loops, and file operations
Week 3 - Theoretical
Introduction to scientific computing with NumPy
Week 4 - Theoretical
Data structures and data analysis with Pandas
Week 5 - Theoretical
Data visualization with Matplotlib and Seaborn
Week 6 - Theoretical
Introduction to machine learning: concepts, types, processes
Week 7 - Theoretical
Data preprocessing and data cleaning techniques
Week 8 - Theoretical
Linear regression algorithm and its application
Week 9 - Theoretical
Logistic regression and classification problems
Week 10 - Theoretical
Decision trees and k-nearest neighbors (KNN) algorithm
Week 11 - Theoretical
Support vector machines (SVM) and naive Bayes classifier
Week 12 - Theoretical
K-means and hierarchical clustering
Week 13 - Theoretical
Model evaluation: accuracy, confusion matrix, cross-validation
Week 14 - Theoretical
General review
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Term Project100220
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
PÇ-6
PÇ-7
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