"This work has been prepared to meet the needs of students who expect to become machinists, either in the special line of automobile construction or in the more general lines of the machine shop. It is therefore strictly limited in scope to the needs of those who are entering upon this kind of work, and it treats only of such topics as experience has shown are demanded by the practical machinist who is determined to advance in his vocation."--Preface.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Contains a larger, easier to read two-color format with improved flow between topics.Provides clear explanations that build on the strengths which have made this book a standard for more than 25 years.Includes an introduction to Statistics which is needed for many technical trades and not offered in most similar texts.Presents sufficient material for a very full one-semester course or for two standard lecture courses.