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Elements of data science, machine le...

Emmert-Streib, Frank.

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Elements of data science, machine learning, and artificial intelligence using R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Elements of data science, machine learning, and artificial intelligence using R/ by Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer.
作者:	Emmert-Streib, Frank.
其他作者:	Moutari, Salissou.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xix, 575 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-031-13339-8
ISBN:	9783031133398

Elements of data science, machine learning, and artificial intelligence using R
Emmert-Streib, Frank.

Elements of data science, machine learning, and artificial intelligence using R[electronic resource] /by Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer. - Cham :Springer International Publishing :2023. - xix, 575 p. :ill. (chiefly color), digital ;24 cm.

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.

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.

ISBN: 9783031133398

Standard No.: 10.1007/978-3-031-13339-8doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

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505 0 $a 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.
520 $a 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.
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