Mathematics for Machine Learning, (Hardcover)
This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
Mathematics for Machine Learning, (Hardcover)
Item #: 254230054

Mathematics for Machine Learning, (Hardcover)

Item #: 254230054

SAR 440

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    This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
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    Shop Mathematics for Machine Learning, (Hardcover) online at a best price in Saudi Arabia. 139671238
    • 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.
    Book formatHardcover
    Fiction/nonfictionNon-Fiction
    GenreComputing & Internet
    Publication dateApril, 2020
    Pages390
    Reading levelGeneral
    SubgenreArtificial Intelligence
    Series titleNo Series
    Edition1
    PublisherCambridge University Press
    Original languagesEnglish
    LanguageEnglish
    Edu focusMath & Counting
    Educational levelCollege
    Is collectibleN
    Binding typeCase Binding
    Recording time0 min
    Retail packagingSingle Piece
    Assembled product dimensions (l x w x h)6.40 x 0.80 x 8.90 in (16.3 x 2 x 22.6 cm)
    Assembled product weight2.1 lb (950 grams)
    Bisac subject headingComputers

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    Mathematics for Machine Learning, (Hardcover)

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    Marc Peter Deisenroth All Books Editorial Review

    Mathematics for Machine Learning is a hardcover non-fiction book published by Cambridge University Press in April 2020. Spanning 390 pages, it covers essential mathematical concepts necessary for understanding machine learning, making it ideal for college-level readers. This book is categorized within the Computing & Internet genre and specifically addresses topics in Artificial Intelligence, which is crucial for anyone looking to dive deeper into the field of machine learning. Its informative content has been positively received by readers seeking to strengthen their math foundation for advanced computing studies.

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    Pros

    • Comprehensive coverage of necessary mathematical concepts
    • Designed for college-level understanding
    • High-quality hardcover format
    • Focuses on Artificial Intelligence applications
    • Useful for both beginners and advanced learners

    Cons

    • Some readers may find it dense in parts

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