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001454822 001__ 1454822
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001454822 019__ $$a1370913350
001454822 020__ $$a9783031174834$$q(electronic bk.)
001454822 020__ $$a3031174836$$q(electronic bk.)
001454822 020__ $$z9783031174827
001454822 020__ $$z3031174828
001454822 0247_ $$a10.1007/978-3-031-17483-4$$2doi
001454822 035__ $$aSP(OCoLC)1371105639
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001454822 050_4 $$aQA76.9.B45
001454822 08204 $$a005.7$$223
001454822 1001_ $$aDinov, Ivo D.,$$eauthor.
001454822 24510 $$aData science and predictive analytics :$$bbiomedical and health applications using R /$$cIvo D. Dinov.
001454822 250__ $$aSecond edition.
001454822 264_1 $$aCham :$$bSpringer,$$c2022.
001454822 300__ $$a1 online resource (1 volume)
001454822 336__ $$atext$$btxt$$2rdacontent
001454822 337__ $$acomputer$$bc$$2rdamedia
001454822 338__ $$aonline resource$$bcr$$2rdacarrier
001454822 4901_ $$aThe Springer series in applied machine learning
001454822 5050_ $$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.
001454822 506__ $$aAccess limited to authorized users.
001454822 520__ $$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 books 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.
001454822 588__ $$aDescription based on print version record.
001454822 650_0 $$aBig data.
001454822 650_0 $$aMathematical statistics.
001454822 650_0 $$aR (Computer program language)
001454822 650_0 $$aBiomedical engineering$$xData processing.
001454822 650_0 $$aMedical informatics.
001454822 655_0 $$aElectronic books.
001454822 77608 $$iPrint version:$$aDinov, Ivo D.$$tData science and predictive analytics.$$bSecond edition.$$dCham : Springer, 2022$$z9783031174827$$w(OCoLC)1348994370
001454822 830_0 $$aSpringer series in applied machine learning.
001454822 852__ $$bebk
001454822 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-17483-4$$zOnline Access$$91397441.1
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001454822 980__ $$aBIB
001454822 980__ $$aEBOOK
001454822 982__ $$aEbook
001454822 983__ $$aOnline
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