001439512 000__ 03828cam\a2200589\i\4500
001439512 001__ 1439512
001439512 003__ OCoLC
001439512 005__ 20230309004432.0
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001439512 007__ cr\un\nnnunnun
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001439512 019__ $$a1267764441
001439512 020__ $$a9783030709013$$q(electronic bk.)
001439512 020__ $$a3030709019$$q(electronic bk.)
001439512 020__ $$z9783030709006
001439512 020__ $$z3030709000
001439512 0247_ $$a10.1007/978-3-030-70901-3$$2doi
001439512 035__ $$aSP(OCoLC)1267337882
001439512 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dN$T$$dOCLCF$$dUKAHL$$dOCLCQ$$dSXT$$dOCLCO$$dOCLCQ
001439512 049__ $$aISEA
001439512 050_4 $$aQA273.19.E4$$bN39 2021
001439512 08204 $$a519.2$$223
001439512 1001_ $$aNazarathy, Yoni,$$eauthor.
001439512 24510 $$aStatistics with Julia :$$bfundamentals for data science, machine learning and artificial intelligence /$$cYoni Nazarathy, Hayden Klok.
001439512 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001439512 264_4 $$c©2021
001439512 300__ $$a1 online resource :$$billustrations (chiefly color)
001439512 336__ $$atext$$btxt$$2rdacontent
001439512 337__ $$acomputer$$bc$$2rdamedia
001439512 338__ $$aonline resource$$bcr$$2rdacarrier
001439512 4901_ $$aSpringer series in the data sciences,$$x2365-5682
001439512 504__ $$aIncludes bibliographical references and index.
001439512 5050_ $$aIntroducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models -- Appendix A: How-to in Julia -- Appendix B: Additional Julia Features -- Appendix C: Additional Packages.
001439512 506__ $$aAccess limited to authorized users.
001439512 520__ $$aThis monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book's associated GitHub repository online.
001439512 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 16, 2021).
001439512 650_0 $$aProbabilities$$xData processing.
001439512 650_0 $$aStatistics$$xData processing.
001439512 650_0 $$aJulia (Computer program language)
001439512 650_6 $$aProbabilités$$xInformatique.
001439512 650_6 $$aStatistique$$xInformatique.
001439512 650_6 $$aJulia (Langage de programmation)
001439512 655_0 $$aElectronic books.
001439512 7001_ $$aKlok, Hayden,$$eauthor.
001439512 77608 $$iPrint version:$$z3030709000$$z9783030709006$$w(OCoLC)1237349017
001439512 830_0 $$aSpringer series in the data sciences.$$x2365-5682
001439512 852__ $$bebk
001439512 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-70901-3$$zOnline Access$$91397441.1
001439512 909CO $$ooai:library.usi.edu:1439512$$pGLOBAL_SET
001439512 980__ $$aBIB
001439512 980__ $$aEBOOK
001439512 982__ $$aEbook
001439512 983__ $$aOnline
001439512 994__ $$a92$$bISE