Sections

• Foreword
• Preface
• Acknowledgments
• 1. General Elements of Genomic Selection and Statistical Learning
• 2. Preprocessing Tools for Data Preparation
• 3. Elements for Building Supervised Statistical Machine Learning Models
• 4. Overfitting, Model Tuning, and Evaluation of Prediction Performance
• 5. Linear Mixed Models
• 6. Bayesian Genomic Linear Regression
• 7. Bayesian and Classical Prediction Models for Categorical and Count Data
• 8. Reproducing Kernel Hilbert Spaces Regression and Classification Methods
• 9. Support Vector Machines and Support Vector Regression
• 10. Fundamentals of Artificial Neural Networks and Deep Learning
• 11. Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes
• 12. Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes
• 13. Convolutional Neural Networks
• 14. Functional Regression
• 15. Random Forest for Genomic Prediction