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Cambridge University Press Cambridge Mathematics for Machine Learning Textbook

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Description

Master the mathematical foundations of data science with this comprehensive textbook from Cambridge University Press. This self-contained resource bridges the gap between pure mathematics and machine learning applications, making it easier for computer science students and professionals to build their knowledge. Instead of searching through disparate courses, readers can access a unified guide covering linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. Designed with a minimum of prerequisites, this book introduces essential concepts and uses them to derive four central machine learning algorithms. It serves as a vital tool for anyone looking to understand the underlying mechanics of modern machine learning technologies through a structured, academic approach.

Key Features

This Cambridge University Press textbook provides a self-contained guide to the mathematical tools required for machine learning.

The content covers essential topics including linear algebra, analytic geometry, matrix decompositions, and vector calculus.

Readers will gain expertise in optimization, probability, and statistics to better understand data science principles.

The text uses mathematical concepts to derive four central machine learning algorithms for practical application.

Designed for students and professionals, this book minimizes prerequisites to help learners bridge the gap between math and machine learning.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
23 April 2020
Listed Since
26 April 2019

Barcode

No barcode data available

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