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.
Author(s): Frank Emmert-Streib; Salissou Moutari; Matthias Dehmer
Publisher: Springer International Publishing
Year: 2023
Language: English
Pages: 1037
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
