02583cam a22003258i 450000100090000000300040000900500170001300800410003001000170007102000310008804000290011904200080014805000220015608200270017810000370020524500950024226300090033726400680034630000230041433600260043733700280046333800270049150400510051850503810056952010240095065000350197470000300200970000300203977601880206921336577OSt20251027205724.0191130s2020    enk      b    001 0 eng    a  2019040762  a9781108455145q(paperback)  aLBSOR/DLCbengerdacDLC  apcc00aQ325.5b.D45 202000a006.31 bD325 20202231 aDeisenroth, Marc Peter,eauthor.10aMathematics for machine learning /cMarc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.  a1912 1aCambridge ;aNew York, NY :bCambridge University Press,c2020.  axvii, 371 pages cm  atextbtxt2rdacontent  aunmediatedbn2rdamedia  avolumebnc2rdacarrier  aIncludes bibliographical references and index.0 aIntroduction 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.  a"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. 0aMachine learningxMathematics.1 aFaisal, A. Aldo,eauthor.1 aOng, Cheng Soon,eauthor.08iOnline version:aDeisenroth, Marc Peter.tMathematics for machine learning.dCambridge, United Kingdom ; New York : Cambridge University Press, 2020.z9781108679930w(DLC)  2019040763