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Information
| Code | SD0675 |
| Name | Introduction to Machine Learning |
| Term | 2024-2025 Academic Year |
| Term | Fall |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Üniversite Dersi |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör.Dr. YILMAZ KOÇAK |
| Course Instructor |
Öğr. Gör.Dr. YILMAZ KOÇAK
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To provide basic knowledge about statistics and machine learning concepts and methods, to understand the general structure of machine learning algorithms, and to gain the ability to code machine learning algorithms with a chosen programming language (Python etc.).
Course Content
Basic statistics, definition and general structure of algorithm of machine learning, coding of machine learning algorithms with the selected programming language, regression and classification algorithms, Support Vector Machines.
Course Precondition
Resources
Uğuz S., Makine Öğrenmesi Teorik Yönleri ve Pyhton Uygulaması, Nobel Yayınları 2. Basım, 2021
Notes
Smola, A. and Vishwanathan, S.V.N. Introduction to Machine Learning, Yahoo Labs, Santa Clara
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains basic concept of statistics. |
| LO02 | Explains the concept and algorithms of Machine Learning |
| LO03 | Codes the Data Preprocessing Process |
| LO04 | Explains regression concepts and defines performance metrics for regression, |
| LO05 | Writes programs to solve simple and multiple linear regression problems. |
| LO06 | Explains the K-Nearest Neighbor (KNN) algorithm |
| LO07 | Defines classification and performance metrics for classification. |
| LO08 | Explains the concepts of Support Vector Machines |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | The importance of using Python in statistics and machine learning | Examining Python Programming Language from source books and search engines | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Examining Python Libraries from source books and search engines. | Examining Python Libraries from source books and search engines. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 3 | Examining Data Visualization with Python. | Examining Data Visualization with Python. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 4 | Vectors and Matrices | Researching of Vectors and Matrices. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Basic Concepts in Machine Learning | Reading the subject from reference books | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Application Development Processes in Machine Learning | Reading the subject from reference books | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 7 | Data Preprocessing | Exploring the concept of Data Preprocessing | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Simple Linear Regression | Researching the concept of regression | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 10 | Multiple Linear Regression | Researching of regression types | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Performance Benchmarks for Regression | Researching performance evaluation | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Bayes Theorem and Classification | Researching the concept of classification | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 13 | Investigation of classification criteria | Investigation of classification criteria | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 14 | K-Nearest Neighbor Algorithm | Investigating the concept of neighborhood | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 15 | Support Vector Machines | Researching the subject. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 16 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 10 | 10 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 10 | 10 |
| Total Workload (Hour) | 82 | ||
| Total Workload / 25 (h) | 3,28 | ||
| ECTS | 3 ECTS | ||