Description
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.
This is an excellent intro to the mathematics behind machine learning. It doesn’t go so deep that you fall asleep but it also doesn’t gloss over some very important details.
Having said that, there is no reason to buy this book as it is free for download from GitHub.
La entrega muy bien. El Producto estaba muy protegido y llegó intacto.
arrived
The treatment of linear algebra is very interesting. After reading the book, I am able to connect many dots in this subject.
Good book