MARC details
000 -LIDER |
fixed length control field |
05772nam a22006375i 4500 |
001 - CONTROL NUMBER |
control field |
978-3-031-17483-4 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
DE-He213 |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20240207153511.0 |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
fixed length control field |
cr nn 008mamaa |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
230216s2023 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9783031174834 |
-- |
978-3-031-17483-4 |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
Classification number |
Q336 |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
UN |
Source |
bicssc |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
COM031000 |
Source |
bisacsh |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
UN |
Source |
thema |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
005.7 |
Edition number |
23 |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Dinov, Ivo D. |
Relator term |
author. |
Authority record control number |
(orcid)0000-0003-3825-4375 |
-- |
https://orcid.org/0000-0003-3825-4375 |
Relator code |
aut |
-- |
http://id.loc.gov/vocabulary/relators/aut |
245 10 - TITLE STATEMENT |
Title |
Data Science and Predictive Analytics |
Medium |
[electronic resource] : |
Remainder of title |
Biomedical and Health Applications using R / |
Statement of responsibility, etc. |
by Ivo D. Dinov. |
250 ## - EDITION STATEMENT |
Edition statement |
2nd ed. 2023. |
264 #1 - |
-- |
Cham : |
-- |
Springer International Publishing : |
-- |
Imprint: Springer, |
-- |
2023. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
XXXIV, 918 p. 336 illus., 306 illus. in color. |
Other physical details |
online resource. |
336 ## - |
-- |
text |
-- |
txt |
-- |
rdacontent |
337 ## - |
-- |
computer |
-- |
c |
-- |
rdamedia |
338 ## - |
-- |
online resource |
-- |
cr |
-- |
rdacarrier |
347 ## - |
-- |
text file |
-- |
PDF |
-- |
rda |
490 1# - SERIES STATEMENT |
Series statement |
The Springer Series in Applied Machine Learning, |
International Standard Serial Number |
2520-1301 |
500 ## - GENERAL NOTE |
General note |
Acceso multiusuario |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
Chapter 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 ## - SUMMARY, ETC. |
Summary, etc. |
Complementary 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 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
Owner |
UABC ; |
Method of acquisition |
Perpetuidad |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Artificial intelligence |
Subdivisión general |
Data processing. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Quantitative research. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Machine learning. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Medical informatics. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Big data. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Data mining. |
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Data Science. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Data Analysis and Big Data. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Machine Learning. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Health Informatics. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Big Data. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Término temático o nombre geográfico como elemento de entrada |
Data Mining and Knowledge Discovery. |
710 2# - ADDED ENTRY--CORPORATE NAME |
Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY |
Title |
Springer Nature eBook |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Relationship information |
Printed edition: |
International Standard Book Number |
9783031174827 |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Relationship information |
Printed edition: |
International Standard Book Number |
9783031174841 |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Relationship information |
Printed edition: |
International Standard Book Number |
9783031174858 |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
Uniform title |
The Springer Series in Applied Machine Learning, |
-- |
2520-1301 |
856 40 - ELECTRONIC LOCATION AND ACCESS |
Public note |
Libro electrónico |
Uniform Resource Identifier |
http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-17483-4 |
912 ## - |
-- |
ZDB-2-SCS |
912 ## - |
-- |
ZDB-2-SXCS |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Koha item type |
Libro Electrónico |