Elements of Data Science, Machine Learning, and Artificial Intelligence Using R 9783031133398, 9783031133381
The textbook provides students with tools they need to analyze complex data using methods from data science, machine lea
139 50 24MB
English Pages 1037 Year 2023
Table of contents :
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
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
- Author / Uploaded
- Frank Emmert-Streib
- Salissou Moutari
- Matthias Dehmer
- Similar Topics
- Computers
- Algorithms and Data Structures: Pattern Recognition
