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Mathematics for Machine Learning

Authors

Marc Peter Deisenroth
, University College London,
A. Aldo Faisal
, Imperial College London,
Cheng Soon Ong
, Data61, CSIRO
Published 2020

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…

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Key features

  • A one-stop presentation of all the mathematical background needed for machine learning
  • Worked examples make it easier to understand the theory and build both practical experience and intuition
  • Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines

Awards

  • Finalist, 2021 PROSE Award - Textbook in the Physical Sciences and Mathematics, Association of American Publishers

About the book

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