LEADER 03467nam 22005413 450 001 9910795555703321 005 20230630002756.0 010 $a1-68392-640-4 010 $a1-68392-641-2 035 $a(MiAaPQ)EBC6837308 035 $a(Au-PeEL)EBL6837308 035 $a(CKB)20343336100041 035 $a(OCoLC)1291317650 035 $a(DE-B1597)654107 035 $a(DE-B1597)9781683926412 035 $a(BIP)081978247 035 $a(EXLCZ)9920343336100041 100 $a20220105d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Science for IoT Engineers $eA Systems Analytics Approach 210 1$aBloomfield :$cMercury Learning & Information,$d2021. 210 4$d©2021. 215 $a1 online resource (170 pages) 311 08$aPrint version: Madhavan, P. G. Data Science for IoT Engineers Bloomfield : Mercury Learning & Information,c2021 9781683926429 327 $tFrontmatter -- $tContents -- $tPreface -- $tAbout the Author -- $tPART I Machine Learning from Multiple Perspectives -- $tCHAPTER 1 Overview of Data Science -- $tCHAPTER 2 Introduction to Machine Learning -- $tCHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics -- $tCHAPTER 4 ?Modern? Machine Learning -- $tPART II Systems Analytics -- $tCHAPTER 5 Systems Theory Foundations of Machine Learning -- $tCHAPTER 6 State Space Model and Bayes Filter -- $tCHAPTER 7 The Kalman Filter for Adaptive Machine Learning -- $tCHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation -- $tCHAPTER 9 Digital Twins -- $tEpilogue A New Random Field Theory -- $tIndex 330 $aThis book introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code that creates a common framework and points out various interconnections related to industry. This will allow the reader to connect previous subject knowledge to data science, machine learning, or analytics and apply it to IoT applications. Part One brings together subjects in machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two (Systems Analytics) develops a ?universal? nonlinear, time-varying dynamical machine learning solution that can faithfully model all the essential complexities of real-life business problems and shows how to apply it. FEATURES:Develops a ?universal,? nonlinear, dynamical machine learning solution to model and apply the complexities of modern applications in IoTCovers topics such as machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins. 606 $aCOMPUTERS / Desktop Applications / Presentation Software$2bisacsh 610 $aIOT. 610 $aMATLAB. 610 $acomputer science. 610 $adata analytics. 610 $aengineering. 610 $amathematics. 610 $aphysics. 615 7$aCOMPUTERS / Desktop Applications / Presentation Software. 676 $a006.312024004678 700 $aMadhavan$b P. G$01465196 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910795555703321 996 $aData Science for IoT Engineers$93675079 997 $aUNINA