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LEADER 00000cam  22003858i 4500 
005    20191218174402.0 
008    191130s2020    enk      b    001 0 eng   
010    2019040762 
019    21336577 
020    9781108470049|q(hardback) 
020    9781108455145|q(paperback) 
020    |z9781108679930|q(epub) 
040    LBSOR/DLC|beng|erda|cDLC 
042    pcc 
050 00 Q325.5|b.D45 2020 
100 1  Deisenroth, Marc Peter,|eauthor 
245 10 Mathematics for machine learning /|cMarc Peter Deisenroth,
       A. Aldo Faisal, Cheng Soon Ong. 
260    Cambridge ;|aNew York, NY :|bCambridge University Press,
       |c2020. 
300    xvii, 371 p. :|bill ;|c cm 
504    Includes bibliographical references and index. 
505 0  Introduction and motivation -- Linear algebra -- Analytic 
       geometry -- Matrix decompositions -- Vector calculus -- 
       Probability and distribution -- Continuous optimization --
       When models meet data -- Linear regression -- 
       Dimensionality reduction with principal component analysis
       -- Density estimation with Gaussian mixture models -- 
       Classification with support vector machines. 
520    "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"-
       -|cProvided by publisher. 
650  0 Machine learning|xMathematics 
700 1  Faisal, A. Aldo,|eauthor 
700 1  Ong, Cheng Soon,|eauthor 
776 08 |iOnline version:|aDeisenroth, Marc Peter.|tMathematics 
       for machine learning.|dCambridge, United Kingdom ; New 
       York : Cambridge University Press, 2020.|z9781108679930
       |w(DLC)  2019040763