TÜRKÇE
ENGLISH
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 |
| Label | NFE Non-Field Elective Courses (University) UCC University Common Course |
| 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 | |
| 2 | Examining Python Libraries from source books and search engines. | Examining Python Libraries from source books and search engines. | |
| 3 | Examining Data Visualization with Python. | Examining Data Visualization with Python. | |
| 4 | Vectors and Matrices | Researching of Vectors and Matrices. | |
| 5 | Basic Concepts in Machine Learning | Reading the subject from reference books | |
| 6 | Application Development Processes in Machine Learning | Reading the subject from reference books | |
| 7 | Data Preprocessing | Exploring the concept of Data Preprocessing | |
| 8 | Mid-Term Exam | Exam preparation | |
| 9 | Simple Linear Regression | Researching the concept of regression | |
| 10 | Multiple Linear Regression | Researching of regression types | |
| 11 | Performance Benchmarks for Regression | Researching performance evaluation | |
| 12 | Bayes Theorem and Classification | Researching the concept of classification | |
| 13 | Investigation of classification criteria | Investigation of classification criteria | |
| 14 | K-Nearest Neighbor Algorithm | Investigating the concept of neighborhood | |
| 15 | Support Vector Machines | Researching the subject. | |
| 16 | Term Exams | Exam preparation | |
| 17 | Term Exams | Exam preparation |
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 | ||