Mathematics for Machine Learning - Info and Reading Options

The Open Library:

"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"--

Search for “Mathematics for Machine Learning” downloads:

Visit our Downloads Search page to see if downloads are available.

Find “Mathematics for Machine Learning” in Libraries Near You:

Read or borrow “Mathematics for Machine Learning” from your local library.

• The WorldCat Libraries Catalog: Find a copy of “Mathematics for Machine Learning” at a library near you.