Overview
- Provides students with tools they need to analyze complex data using methods from data science
- Presents the tools students need to analyze data using the R programming language
- Includes a full suite of classroom materials including exercises, Q&A, and examples
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Table of contents (18 chapters)
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Front Matter
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General Topics
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Front Matter
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Core Methods
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Front Matter
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Advanced Topics
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Front Matter
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Keywords
About this book
Authors and Affiliations
About the authors
Frank Emmert-Streib is Professor of Data Science at Tampere University (Finland). He leads the Predictive Society and Data Analytics Lab, which pursues innovative research in deep learning and natural language processing. The Lab develops and applies high-dimensional methods in machine learning, statistics, and artificial intelligence that can be used to extract knowledge from data in the fields of biology, medicine, social media, social sciences, marketing, or business.
Salissou Moutari is Senior Lecturer at Queen’s University Belfast (UK) and Interim Director of Research of the Mathematical Science Research Centre (MSRC). His research interests include mathematical modelling, optimization, machine learning and data science, and the applications of these methods to problems from traffic, transportation and distribution systems, production planning and industrial processes.
Matthias Dehmer is Professor at UMIT (Austria) and also has a position at Swiss Distance University of Applied Sciences, Brig, Switzerland. His research interests are in complex networks, complexity, data science, machine learning, big data analytics, and information theory. In particular, he is working on machine learning based methods to analyse high-dimensional data.
Bibliographic Information
Book Title: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
Authors: Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer
DOI: https://doi.org/10.1007/978-3-031-13339-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-13338-1Published: 04 October 2023
Softcover ISBN: 978-3-031-13341-1Due: 29 November 2023
eBook ISBN: 978-3-031-13339-8Published: 03 October 2023
Edition Number: 1
Number of Pages: XIX, 575
Number of Illustrations: 6 b/w illustrations, 156 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Communications Engineering, Networks, Machine Learning, Data Mining and Knowledge Discovery