Emmert-Streib F., Moutari S., Dehmer M. Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Springer, 2023. — 581 p. — ISBN 3031133382.The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.Preface.
Introduction to Learning from Data.
General Topics
General Prediction Models.
General Error Measures.
Resampling Methods.
Data.
Core Methods
Statistical Inference.
Clustering.
Dimension Reduction.
Classification.
Hypothesis Testing.
Linear Regression Models.
Model Selection.
Advanced Topics
Regularization.
Deep Learning.
Multiple Testing Corrections.
Survival Analysis.
Foundations of Learning from Data.
Generalization Error and Model Assessment.
References
IndexTrue PDF