Mathematics in machine learning. Daisenrot, Feizal, he is Ch.

The fundamental mathematical disciplines necessary for understanding machine learning are a linear algebra, analytical geometry, vector analysis, optimization, probability theory and statistics. Traditionally, all these topics are smeared in various courses, so students studying Data Science or Computer Science, as well as professionals in the Moscow Region, difficult to build knowledge into a single concept.

This book is self -sufficient: the reader gets acquainted with basic mathematical concepts, And then it goes to the four main methods of the Moscow Region: linear regression, the method of the main components, Gaussian modeling and the method of support vectors. Those who are just starting to study mathematics will help to 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.