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Multivariate statistics and machine learning in R for beginners = with applications in biology and medicine /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multivariate statistics and machine learning in R for beginners/ by Andreas Tilevik.
Reminder of title:	with applications in biology and medicine /
Author:	Tilevik, Andreas.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xviii, 398 p. :ill., digital ;24 cm.
[NT 15003449]:	A brief introduction to machine learning and multivariate statistics -- Matrix algebra -- Managing data in R -- Graphical illustration of multivariate data -- Multivariate relationships -- PCA and PCoA -- Linear discriminant analysis -- Distances in space -- Multivariate statistical tests -- Classification and performance metrics -- Supervised Machine Learning -- Clustering -- PCR, PLS and Lasso regression -- Case studies -- Anwers to exercises.
Contained By:	Springer Nature eBook
Subject:	Multivariate analysis. -
Online resource:	https://doi.org/10.1007/978-3-032-01851-9
ISBN:	9783032018519

Multivariate statistics and machine learning in R for beginners = with applications in biology and medicine /
Tilevik, Andreas.

Multivariate statistics and machine learning in R for beginnerswith applications in biology and medicine /[electronic resource] :by Andreas Tilevik. - Cham :Springer Nature Switzerland :2025. - xviii, 398 p. :ill., digital ;24 cm.

A brief introduction to machine learning and multivariate statistics -- Matrix algebra -- Managing data in R -- Graphical illustration of multivariate data -- Multivariate relationships -- PCA and PCoA -- Linear discriminant analysis -- Distances in space -- Multivariate statistical tests -- Classification and performance metrics -- Supervised Machine Learning -- Clustering -- PCR, PLS and Lasso regression -- Case studies -- Anwers to exercises.

This book is more than just a book - it is a full course designed as an interactive guide for beginners in multivariate analysis. Combining theoretical videos with practical examples in R, it offers readers a unique blend of theory, practice, and application in biology and medicine. In an era where data-driven insights shape every field, mastering multivariate statistics and machine learning techniques has never been more essential. Each chapter links directly to videos, which explain the theoretical foundations of the statistical or machine learning methods in a basic way. Following each video, readers will find R code that replicates the analyses presented in the videos, empowering them to see real-world applications in action. Many exercises are included, allowing the readers to test their understanding of each concept through hands-on practice. The book covers a comprehensive range of essential topics in multivariate statistics and machine learning, including fundamentals of matrix operations, multivariate plotting, and correlation, as well as methods for multivariate data analysis such as multivariate analysis of variance (MANOVA), principal component analysis (PCA), clustering, decision trees, discriminant analysis, random forest, partial least squares (PLS), canonical correlation analysis (CCA) and survival analysis. It also includes two case studies that reproduce the multivariate analyses in two scientific papers related to drug discovery and biomarker identification. By integrating videos with practical coding examples, this text makes complex topics accessible for beginners. The interactive learning approach ensures that readers not only grasp the statistical theories and machine learning concepts but also gain the confidence to apply them effectively in real-world scenarios.

ISBN: 9783032018519

Standard No.: 10.1007/978-3-032-01851-9doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Multivariate statistics and machine learning in R for beginners = with applications in biology and medicine /
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505 0 $a A brief introduction to machine learning and multivariate statistics -- Matrix algebra -- Managing data in R -- Graphical illustration of multivariate data -- Multivariate relationships -- PCA and PCoA -- Linear discriminant analysis -- Distances in space -- Multivariate statistical tests -- Classification and performance metrics -- Supervised Machine Learning -- Clustering -- PCR, PLS and Lasso regression -- Case studies -- Anwers to exercises.
520 $a This book is more than just a book - it is a full course designed as an interactive guide for beginners in multivariate analysis. Combining theoretical videos with practical examples in R, it offers readers a unique blend of theory, practice, and application in biology and medicine. In an era where data-driven insights shape every field, mastering multivariate statistics and machine learning techniques has never been more essential. Each chapter links directly to videos, which explain the theoretical foundations of the statistical or machine learning methods in a basic way. Following each video, readers will find R code that replicates the analyses presented in the videos, empowering them to see real-world applications in action. Many exercises are included, allowing the readers to test their understanding of each concept through hands-on practice. The book covers a comprehensive range of essential topics in multivariate statistics and machine learning, including fundamentals of matrix operations, multivariate plotting, and correlation, as well as methods for multivariate data analysis such as multivariate analysis of variance (MANOVA), principal component analysis (PCA), clustering, decision trees, discriminant analysis, random forest, partial least squares (PLS), canonical correlation analysis (CCA) and survival analysis. It also includes two case studies that reproduce the multivariate analyses in two scientific papers related to drug discovery and biomarker identification. By integrating videos with practical coding examples, this text makes complex topics accessible for beginners. The interactive learning approach ensures that readers not only grasp the statistical theories and machine learning concepts but also gain the confidence to apply them effectively in real-world scenarios.
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