Machines that can imitate or even replace human intelligence in information processing are being developed and utilized in various ways, and machine learning is a field of artificial intelligence that provides the basic methodology for this.
In order to take an interest in machine learning and apply it to one's field, it is necessary to not only have an individual understanding of each methodology, but also to comprehensively understand and judge various situations according to the development purpose and environment.
For this, the most important thing is a solid foundation and broad understanding of machine learning techniques.
To this end, this textbook introduces the general knowledge and understanding of machine learning, rather than delving into specific topics within the field.
That is, we attempted to systematically cover the concepts and principles of various methodologies, from basic and traditional methodologies to the latest technologies, and the corresponding basic algorithms.

This textbook consists of a total of 14 chapters.
*Chapter 1: Introduces basic concepts and terminology related to machine learning.
*Chapter 2-3: Machine learning requires a variety of mathematical knowledge.
A variety of subjects are required, including linear algebra, probability, and statistics, as well as differential geometry and differential equations depending on the application.
However, here we will introduce the basic concepts related to vectors, matrices, and probability and statistics.
*Chapters 4-7: We will look at the main techniques related to the four topics covered in machine learning: classification, regression, clustering, and feature extraction.
*Chapter 8-10: Covers ensemble learning, decision trees, random forests, and SVMs, chapter by chapter.
*Chapters 11-14: Learn about neural networks, deep learning, a form of machine learning technique developed based on neural networks, and reinforcement learning.