Mathematics for Machine Learning
Mathematics for Machine Learning uses concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.
The basic mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decomposition, vector calculus, optimization, probability, and statistics. Because these topics are traditionally taught in a variety of courses, it is difficult for data science or computer science students or professionals to learn mathematics effectively. The authors Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong came up with the book Mathematics for Machine Learning to address these challenges.
This book bridges the gap between math and machine learning texts by introducing math concepts with minimal prerequisites. The book Mathematics for Machine Learning uses these concepts to derive four main machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and other professionals with a math background, these results serve as a starting point for machine learning texts. For students new to mathematics, these methods help develop intuition in the application of mathematical concepts and gain practical experience. Each chapter includes elaborate examples and exercises to test your comprehension. A programming tutorial is available on the book's website.The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Why this book
The topics covered in this book are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This book bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
Concepts covered
This book uses the following concepts to derive four central machine learning methods:
- linear regression,
- principal component analysis,
- Gaussian mixture models and
- support vector machines.
Who is this book for
The book Mathematics for Machine Learning is 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.