Mathematics for Machine Learning
Cambridge University Press
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Mathematics for Machine Learning

About this item

  • 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.

At a glance

Book Format
Paperback
Genre
Textbooks
Publication Date
April 23, 2020
Number of Pages
398
Reading Level
ADULT
Age Range
15 Years & Up

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