02465nam 22004093 450 991102195480332120250618080336.01-64336-636-X(MiAaPQ)EBC32006238(Au-PeEL)EBL32006238(CKB)39300711200041(OCoLC)1524424334(EXLCZ)993930071120004120250618d2025 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAtlas of South Carolina3rd ed.Columbia :University of South Carolina Press,2025.©2025.1 online resource (46 pages)1-64336-626-2 Cover -- Title -- CONTENTS -- 1. Location -- Location -- Counties and County Seats -- 2. Natural Landscape -- Elevation -- Landform Regions -- Earthquakes -- Land Cover -- Biodiversity and Conservation -- Rivers, Lakes, and Bays -- Climate Regions -- Temperature and Precipitation -- Tornadoes -- Hurricanes -- Changing Environment -- 3. Early Human Landscape -- Native Americans, ca. 1670 -- European Contact -- 4. Colonial Landscape -- Generalized Patterns of Colonial Agriculture -- Township System, 1731-1765 -- Colonial Roads and Revolutionary War Engagements -- 5. Antebellum Landscape -- Population Change, 1800-1860 -- Agriculture and Industry, ca. 1840 -- Slavery -- Gullah Geechee Communities in Coastal South Carolina -- Canals and Railroads -- Civil War -- 6. Postbellum Landscape -- Population, 1900-1950 -- Black Population, 1900-1940 -- Tenancy and Tobacco -- Textiles and Railroads -- Civil Rights Era -- 7. Contemporary Landscape -- Population, 1970-2020 -- Cities and Metropolitan Statistical Areas -- Population Born and Residing in South Carolina -- Race and Ethnicity -- Education -- Higher Education -- Military Landscape -- Agriculture -- Industry -- Transportation -- Energy Landscape -- State and National Parks -- Tourism -- Religion -- Barbeque Regions -- College Football -- Electoral Geography -- Geographic Regions -- Tables -- Copyright.Crane Austin1846520Mewborne Michael1846521Fleming Jory1846522Bentley Elbie1846523MiAaPQMiAaPQMiAaPQBOOK9911021954803321Atlas of South Carolina4431156UNINA05141nam 22006855 450 991102196290332120250825130208.09783031937644(electronic bk.)978303193763710.1007/978-3-031-93764-4(MiAaPQ)EBC32271006(Au-PeEL)EBL32271006(CKB)40416498400041(DE-He213)978-3-031-93764-4(OCoLC)1534435187(EXLCZ)994041649840004120250825d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLinear Algebra, Data Science, and Machine Learning /by Jeff Calder, Peter J. Olver1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (645 pages)Springer Undergraduate Texts in Mathematics and Technology,1867-5514Print version: Calder, Jeff Linear Algebra, Data Science, and Machine Learning Cham : Springer,c2025 9783031937637 Preface -- 1 Vectors -- 2 Inner Product, Orthogonality, Norm -- 3 Matrices -- 4. How Matrices Interact with Inner Products and Norms -- 5 Eigenvalues and Singular Values -- 6 Basics of Optimization -- 7 Introduction to Machine Learning and Data -- 8 Principal Component Analysis -- 9 Graph Theory and Graph-based Learning -- 10 Neural Networks and Deep Learning -- 11 Advanced Optimization -- Bibliography -- Index.This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics. To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website. The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.Springer Undergraduate Texts in Mathematics and Technology,1867-5514Algebras, LinearArtificial intelligenceData processingMachine learningMathematical optimizationProbabilitiesLinear AlgebraData ScienceMachine LearningOptimizationApplied ProbabilityAlgebras, Linear.Artificial intelligenceData processing.Machine learning.Mathematical optimization.Probabilities.Linear Algebra.Data Science.Machine Learning.Optimization.Applied Probability.512.5Calder Jeff1845711Olver Peter J40590MiAaPQMiAaPQMiAaPQ9911021962903321Linear Algebra, Data Science, and Machine Learning4429510UNINA