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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.
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1
Mathematics for Machine Learning
2020, Cambridge University Press
Paperback
in English
110845514X 9781108455145
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2
Mathematics for Machine Learning
2020, Cambridge University Press
in English
1108679935 9781108679930
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Mathematics for Machine Learning
2020, Cambridge University Press
in English
1108470041 9781108470049
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Mathematics for Machine Learning
2019, Cambridge University Press
in English
1108569323 9781108569323
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October 12, 2020 | Edited by ImportBot | import existing book |
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