000			03881nam a2200373Ia 4500
000			04991nam a22004095i 4500
001			978-3-031-17483-4
003			DE-He213
005			20240319120803.0
007			cr nn 008mamaa
008			230216s2023 sz | s |||| 0|eng d
020			_a9783031174834 _9978-3-031-17483-4
082			_a5.7
100			_aDinov, Ivo D. _928731
245			_aData Science and Predictive Analytics _cby Ivo D. Dinov. _h[electronic resource] :
250			_a2nd ed. 2023.
260			_aCham _bSpringer International Publishing _c2023
300			_aXXXIV, 918 p. 336 illus., 306 illus. in color. _bonline resource.
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.
650			_aArtificial intelligence _928732
650			_aBig data. _928733
650			_aBig Data. _928734
650			_aData Analysis and Big Data. _928735
650			_aData Mining and Knowledge Discovery. _928736
650			_aData mining. _928737
650			_aData Science. _928738
650			_aHealth Informatics. _928739
650			_aMachine learning. _928740
650			_aMachine Learning. _928741
650			_aMedical informatics. _928742
650			_aQuantitative research. _928743
856			_uhttps://doi.org/10.1007/978-3-031-17483-4
942			_cEBK _2ddc
999			_c14983 _d14983