001482244 000__ 05062cam\\2200601\i\4500
001482244 001__ 1482244
001482244 003__ OCoLC
001482244 005__ 20231128003328.0
001482244 006__ m\\\\\o\\d\\\\\\\\
001482244 007__ cr\un\nnnunnun
001482244 008__ 231007s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001482244 019__ $$a1402816991
001482244 020__ $$a9783031133398$$q(electronic bk.)
001482244 020__ $$a3031133390$$q(electronic bk.)
001482244 020__ $$z9783031133381
001482244 020__ $$z3031133382
001482244 0247_ $$a10.1007/978-3-031-13339-8$$2doi
001482244 035__ $$aSP(OCoLC)1401961443
001482244 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF
001482244 049__ $$aISEA
001482244 050_4 $$aQ335
001482244 08204 $$a006.3$$223/eng/20231016
001482244 1001_ $$aEmmert-Streib, Frank,$$eauthor.
001482244 24510 $$aElements of data science, machine learning, and artificial intelligence using R /$$cFrank Emmert-Streib, Salissou Moutari, Matthias Dehmer.
001482244 264_1 $$aCham :$$bSpringer,$$c[2023]
001482244 264_4 $$c©2023
001482244 300__ $$a1 online resource (xix, 575 pages) :$$billustrations (chiefly color)
001482244 336__ $$atext$$btxt$$2rdacontent
001482244 337__ $$acomputer$$bc$$2rdamedia
001482244 338__ $$aonline resource$$bcr$$2rdacarrier
001482244 504__ $$aIncludes bibliographical references and index.
001482244 5050_ $$aIntroduction -- Introduction to learning from data -- Part 1: General topics -- Prediction models -- Error measures -- Resampling -- Data types -- Part 2: Core methods -- Maximum Likelihood & Bayesian analysis -- Clustering -- Dimension Reduction -- Classification -- Hypothesis testing -- Linear Regression -- Model Selection -- Part 3: Advanced topics -- Regularization -- Deep neural networks -- Multiple hypothesis testing -- Survival analysis -- Generalization error -- Theoretical foundations -- Conclusion.
001482244 506__ $$aAccess limited to authorized users.
001482244 520__ $$aIn recent years, large amounts of data became available in all areas of science, industry and society. This provides unprecedented opportunities for enhancing our knowledge, and to solve scientific and societal problems. In order to emphasize the importance of this, data have been called the "oil of the 21st Century". Unfortunately, data do usually not reveal information easily, but analysis methods are required to extract it. This is the main task of data science. The textbook provides students with tools they need to analyze complex data using methods from machine learning, artificial intelligence and statistics. These are the main fields comprised by data science. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. This allows the immediate practical application of the learning concepts side-by-side. The book advocates an integration of statistical thinking, computational thinking and mathematical thinking because data science is an interdisciplinary field requiring an understanding of statistics, computer science and mathematics. Furthermore, the book highlights the understanding of the domain knowledge about experiments or processes that generate or produce the data. The goal of the authors is to provide students with a systematic approach to data science that allows a continuation of the learning process beyond the presented topics. Hence, the book enables learning to learn. Main features of the book: - emphasizing the understanding of methods and underlying concepts - integrating statistical thinking, computational thinking and mathematical thinking - highlighting the understanding of the data - exploring the power of visualizations - balancing theoretical and practical presentations - demonstrating the application of methods using R - providing detailed examples and discussions - presenting data science as a complex network Elements of Data Science, Machine Learning and Artificial Intelligence using R presents basic, intermediate and advanced methods for learning from data, culminating into a practical toolbox for a modern data scientist. The comprehensive coverage allows a wide range of usages of the textbook from (advanced) undergraduate to graduate courses. .
001482244 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 16, 2023).
001482244 650_6 $$aIntelligence artificielle.
001482244 650_6 $$aApprentissage automatique.
001482244 650_6 $$aR (Langage de programmation)
001482244 650_0 $$aArtificial intelligence.$$xMedical applications$$0(DLC)sh 88003000
001482244 650_0 $$aMachine learning.$$vCongresses$$0(DLC)sh2008107143
001482244 650_0 $$aR (Computer program language)$$0(DLC)sh2002004407
001482244 655_0 $$aElectronic books.
001482244 7001_ $$aMoutari, Salissou,$$eauthor.
001482244 7001_ $$aDehmer, Matthias,$$d1968-$$eauthor.
001482244 77608 $$iPrint version: $$z3031133382$$z9783031133381$$w(OCoLC)1334719347
001482244 852__ $$bebk
001482244 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-13339-8$$zOnline Access$$91397441.1
001482244 909CO $$ooai:library.usi.edu:1482244$$pGLOBAL_SET
001482244 980__ $$aBIB
001482244 980__ $$aEBOOK
001482244 982__ $$aEbook
001482244 983__ $$aOnline
001482244 994__ $$a92$$bISE