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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Mathematics for Machine Learning (Paperback)
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Mathematics for Machine Learning (Paperback)
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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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