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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| COMPUTER SCIENCES PR. | |
| Code | BBZ307 |
| Name | Introduction to Machine Learning |
| Term | 2025-2026 Academic Year |
| Semester | 5. Semester |
| Duration (T+A) | 3-1 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3.5 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | VK Vocational Knowledge Courses C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to understand machine learning algorithms, examine their working principles mathematically, and apply these algorithms to data sets with a programming language.
Course Content
In this course, Machine Learning and Concepts, Supervised Learning Algorithms, Unsupervised Learning Algorithms topics will be covered.
Course Precondition
It is not available.
Resources
N. Gürsakal, Makine Öğrenmesi, Dora Yayın, 2018. ME. Balaban, E. Kartal, Veri Madenciliği ve Makine Öğrenmesi, Çağlayan Yayıncılık, 2018.
Notes
Ders Notları M. Kubat, Introduction to Machine Learning, Springer, 2017. Ethem Alpaydın,Introduction to Machine Learning, MIT Press.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains basic statistical concepts. |
| LO02 | Recognize the concept of Machine Learning and its algorithms. |
| LO03 | Applies methods and techniques for data preprocessing. |
| LO04 | It uses regression and performance measures for regression. |
| LO05 | Applies analysis methods and techniques for simple and multiple linear regression problems. |
| LO06 | It uses performance metrics for classification. |
| LO07 | Recognize the features of the K-Nearest Neighbor Algorithm. |
| LO08 | Explains the Binary Logistic Regression method. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | 2 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | 2 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Machine Learning, basic definitions and concepts. | Reading sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Types of Machine Learning and Performance Evaluation | Reading sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 3 | Pre-Processing for Data: Preprocessing | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 4 | Supervised Learning 1 - Machine Learning Based on Multiple Linear Regression, Ridge Regression and LASSO | Reading sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 5 | Supervised Learning 2 - k-Nearest Neighborhood | Reading sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Supervised Learning 3- Support Vector Machines | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 7 | Supervised Learning 4 - Simple Bayes Classifier | Reading sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Reading lecture notes and resources. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Supervised Learning 5- Binary Logistic Regression | Reading sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 10 | Unsupervised Learning 1- Principal Components Analysis | Reading sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap |
| 11 | Unsupervised Learning 2 - K-Means | Reading sources | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Gösterip Yaptırma |
| 12 | Ensemble Learning- Random Forest | Reading sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 13 | En İyi Modelin Seçimi - K-katlı çapraz doğrulama ve Hiper Parametre Seçimi | Reading sources | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Alıştırma ve Uygulama |
| 14 | Homework Presentations I | Completing the presentation | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 15 | Homework Presentations II | Completing the presentation | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 16 | Term Exams | Reading lecture notes and resources. | Ölçme Yöntemleri: Proje / Tasarım, Yazılı Sınav |
| 17 | Term Exams | Reading lecture notes and resources. | Ölçme Yöntemleri: Proje / Tasarım, Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 3 | 3 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 3 | 3 |
| Final Exam | 1 | 4 | 4 |
| Total Workload (Hour) | 122 | ||
| Total Workload / 25 (h) | 4,88 | ||
| ECTS | 5 ECTS | ||