Mathematics in machine learning
39.99 €
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Mathematics in machine learning
39.99 €
The fundamental mathematical disciplines required to understand machine learning are linear algebra, analytical geometry, vector analysis, optimization, probability theory, and statistics. Traditionally, all these topics are spread across different courses, so it is difficult for students studying data science or computer science, as well as professionals in MO, to build knowledge into a single concept.
This book is self-sufficient: the reader gets acquainted with the basic mathematical concepts, and then proceeds to the four main methods of ML: linear regression, the method of principal components, Gaussian modeling and the method of reference vectors.
For those who are just beginning to study mathematics, this approach will help to develop intuition and gain practical experience in the application of mathematical knowledge.
For readers with a basic mathematical background, the book will serve as a starting point for a more advanced introduction to machine learning.
This book is self-sufficient: the reader gets acquainted with the basic mathematical concepts, and then proceeds to the four main methods of ML: linear regression, the method of principal components, Gaussian modeling and the method of reference vectors.
For those who are just beginning to study mathematics, this approach will help to develop intuition and gain practical experience in the application of mathematical knowledge.
For readers with a basic mathematical background, the book will serve as a starting point for a more advanced introduction to machine learning.
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