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008			230216s2023 sz | o |||| 0|eng d
020			_a9783031174834
024	7		_a10.1007/978-3-031-17483-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .B45 _b2023 EB
100	1		_aDinov, Ivo D. _eautor _0(orcid)0000-0003-3825-4375 _1https://orcid.org/0000-0003-3825-4375 _4http://id.loc.gov/vocabulary/relators/aut _9689496
245	1	0	_aData Science and Predictive Analytics : _bBiomedical and Health Applications using R _cby Ivo D. Dinov
250			_a2nd ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aThe Springer Series in Applied Machine Learning _x2520-1301
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.
988			_aSpringer_Computer_2023
650		7	_2embne _9495511 _aDatos masivos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-17483-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI