Mathematics for Machine Learning
$43.00
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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.
ASIN: 110845514X
VSKU: DBV.110845514X.A
Condition: Acceptable
Author/Artist:Deisenroth, Marc Peter
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: This copy has clearly been enjoyed—expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
ASIN: 110845514X
VSKU: DBV.110845514X.A
Condition: Acceptable
Author/Artist:Deisenroth, Marc Peter
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: This copy has clearly been enjoyed—expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
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