Mathematics in Machine Learning

The fundamental mathematical disciplines necessary for understanding machine learning are linear algebra, analytical geometry, vector analysis, optimization, probability theory, and statistics. Traditionally, all these topics are spread across various courses, making it difficult for students studying data science or computer science, as well as professionals in ML, to structure their knowledge into a coherent concept.

This book is self-sufficient: the reader is introduced to basic mathematical concepts and then moves on to four main methods of ML: linear regression, principal component analysis, Gaussian modeling, and support vector methods.

For those who are just beginning to study mathematics, this approach will help develop intuition and gain practical experience in applying mathematical knowledge,
and for readers with a basic mathematical education, the book will serve as a starting point for a more advanced acquaintance with machine learning.