Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

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. Cover Front Matter 1. Introduction to Learning from Data Part I. General Topics 2. General Prediction Models 3. General Error Measures 4. Resampling Methods 5. Data Part II. Core Methods 6. Statistical Inference 7. Clustering 8. Dimension Reduction 9. Classification 10. Hypothesis Testing 11. Linear Regression Models 12. Model Selection Part III. Advanced Topics 13. Regularization 14. Deep Learning 15. Multiple Testing Corrections 16. Survival Analysis 17. Foundations of Learning from Data 18. Generalization Error and Model Assessment Back Matter