Elements of data science, machine learning, and artificial intelligence using R / Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer.
Emmert-Streib, Frank, author.; Moutari, Salissou, author.; Dehmer, Matthias, 1968- author.
2023
Q335
Available Online

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Title Elements of data science, machine learning, and artificial intelligence using R / Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer.
Author Emmert-Streib, Frank, author.
ISBN 9783031133398 (electronic bk.)
3031133390 (electronic bk.)
9783031133381
3031133382
DOI https://doi.org/10.1007/978-3-031-13339-8
Published Cham : Springer, [2023]
Copyright ©2023
Language English
Description 1 online resource (xix, 575 pages) : illustrations (chiefly color)
Item Number 10.1007/978-3-031-13339-8 doi
Call Number Q335
Dewey Decimal Classification 006.3
Summary In recent years, large amounts of data became available in all areas of science, industry and society. This provides unprecedented opportunities for enhancing our knowledge, and to solve scientific and societal problems. In order to emphasize the importance of this, data have been called the "oil of the 21st Century". Unfortunately, data do usually not reveal information easily, but analysis methods are required to extract it. This is the main task of data science. The textbook provides students with tools they need to analyze complex data using methods from machine learning, artificial intelligence and statistics. These are the main fields comprised by data science. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. This allows the immediate practical application of the learning concepts side-by-side. The book advocates an integration of statistical thinking, computational thinking and mathematical thinking because data science is an interdisciplinary field requiring an understanding of statistics, computer science and mathematics. Furthermore, the book highlights the understanding of the domain knowledge about experiments or processes that generate or produce the data. The goal of the authors is to provide students with a systematic approach to data science that allows a continuation of the learning process beyond the presented topics. Hence, the book enables learning to learn. Main features of the book: - emphasizing the understanding of methods and underlying concepts - integrating statistical thinking, computational thinking and mathematical thinking - highlighting the understanding of the data - exploring the power of visualizations - balancing theoretical and practical presentations - demonstrating the application of methods using R - providing detailed examples and discussions - presenting data science as a complex network Elements of Data Science, Machine Learning and Artificial Intelligence using R presents basic, intermediate and advanced methods for learning from data, culminating into a practical toolbox for a modern data scientist. The comprehensive coverage allows a wide range of usages of the textbook from (advanced) undergraduate to graduate courses. .
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed October 16, 2023).
Added Author Moutari, Salissou, author.
Dehmer, Matthias, 1968- author.
Available in Other Form Print version: 9783031133381
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Introduction
Introduction to learning from data
Part 1: General topics
Prediction models
Error measures
Resampling
Data types
Part 2: Core methods
Maximum Likelihood & Bayesian analysis
Clustering
Dimension Reduction
Classification
Hypothesis testing
Linear Regression
Model Selection
Part 3: Advanced topics
Regularization
Deep neural networks
Multiple hypothesis testing
Survival analysis
Generalization error
Theoretical foundations
Conclusion.

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