000 05772nam a22006375i 4500
001 978-3-031-17483-4
003 DE-He213
005 20240207153511.0
007 cr nn 008mamaa
008 230216s2023 sz | s |||| 0|eng d
020 _a9783031174834
_9978-3-031-17483-4
050 4 _aQ336
072 7 _aUN
_2bicssc
072 7 _aCOM031000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
100 1 _aDinov, Ivo D.
_eauthor.
_0(orcid)0000-0003-3825-4375
_1https://orcid.org/0000-0003-3825-4375
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aData Science and Predictive Analytics
_h[electronic resource] :
_bBiomedical and Health Applications using R /
_cby Ivo D. Dinov.
250 _a2nd ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXXXIV, 918 p. 336 illus., 306 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Springer Series in Applied Machine Learning,
_x2520-1301
500 _aAcceso multiusuario
505 0 _aChapter 1 - Introduction -- Chapter 2: Basic Visualization and Exploratory Data Analytics -- Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling -- Chapter 4: Linear and Nonlinear Dimensionality Reduction -- Chapter 5: Supervised Classification -- Chapter 6: Black Box Machine Learning Methods -- Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning -- Chapter 8: Unsupervised Clustering -- Chapter 9: Model Performance Assessment, Validation, and Improvement -- Chapter 10: Specialized Machine Learning Topics -- Chapter 11: Variable Importance and Feature Selection -- Chapter 12: Big Longitudinal Data Analysis -- Chapter 13: Function Optimization -- Chapter 14: Deep Learning, Neural Networks.
520 _aComplementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in this textbook address specific knowledge gaps, resolve educational barriers, and mitigate workforce information readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical foundations, modern computational methods, advanced data science techniques, model-based machine learning (ML), model-free artificial intelligence (AI), and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build the foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. Individual modules and complete end-to-end pipeline protocols are available as functional R electronic markdown notebooks. These workflows support an active learning platform for comprehensive data manipulation, sophisticated analytics, interactive visualization, and effective dissemination of open problems, current knowledge, scientific tools, and research findings. This Second Edition includes new material reflecting recent scientific and technological progress and a substantial content reorganization to streamline the covered topics. Featured are learning-based strategies utilizing generative adversarial networks (GANs), transfer learning, and synthetic data generation. There are complete end-to-end examples of ML/AI training, prediction, and assessment using quantitative, qualitative, text, and imaging datasets. This textbook is suitable for self-learning and instructor-guided course training. It is appropriate for upper division and graduate-level courses covering applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide spectrum of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory and funding agencies.
541 _fUABC ;
_cPerpetuidad
650 0 _aArtificial intelligence
_xData processing.
650 0 _aQuantitative research.
650 0 _aMachine learning.
650 0 _aMedical informatics.
650 0 _aBig data.
650 0 _aData mining.
650 1 4 _aData Science.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aMachine Learning.
650 2 4 _aHealth Informatics.
650 2 4 _aBig Data.
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031174827
776 0 8 _iPrinted edition:
_z9783031174841
776 0 8 _iPrinted edition:
_z9783031174858
830 0 _aThe Springer Series in Applied Machine Learning,
_x2520-1301
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-17483-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cLIBRO_ELEC
999 _c260801
_d260800