Mathematics for machine learning
- eBook
- Edition:
- 1
- Publisher:
- Cambridge University Press
- Release Date:
- 2019
- ISBN-10:
- 1108569323
- ISBN-13:
- 9781108569323
Buy vs Rent: We recommend you Rent
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Store Condition Coupons & Deals Price Shipping Total eCampus 180 Day eBook
(expires 04/18/2027)180 Day eBook
(expires 04/18/2027)$55.68
as of Oct 20, 2026, 12:29 AM ETFREE eCampus Short Term Rental
(60 days, due 12/19/2026)Short Term Rental
(60 days, due 12/19/2026)$63.94
as of Oct 20, 2026, 12:29 AM ET$3.97 eCampus Quarter Rental
(59 days, due 12/18/2026)Quarter Rental
(59 days, due 12/18/2026)$67.49
as of Oct 20, 2026, 12:29 AM ET$3.97 eCampus Semester Rental
(76 days, due 01/04/2027)Semester Rental
(76 days, due 01/04/2027)$71.04
as of Oct 20, 2026, 12:29 AM ET$3.97 eCampus UsedUsed $85.25
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(60 days, due 12/19/2026)Short Term Rental
(60 days, due 12/19/2026)$63.94
as of Oct 20, 2026, 12:29 AM ET$3.97 -
Store Condition Coupons & Deals Price Shipping Total eCampus 180 Day eBook
(expires 04/18/2027)180 Day eBook
(expires 04/18/2027)$55.68
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About Mathematics for machine learning
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…
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