Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners.
1. Chapter 1. Introduction to machine learning - p. 1 2. Chapter 2. Main concepts in machine learning - p. 21 3. Chapter 3. Applications of machine learning to brain disorders - p. 45 4. Part 2 - p. 65 5. Chapter 4. Linear regression - p. 67 6. Chapter 5. Linear methods for classification - p. 83 7. Chapter 6. Support vector machine - p. 101 8. Chapter 7. Support vector regression - p. 123 9. Chapter 8. Multiple kernel learning - p. 141 10. Chapter 9. Deep neural networks - p. 157 11. Chapter 10. Convolutional neural networks - p. 173 12. Chapter 11. Autoencoders - p. 193 13. Chapter 12. Principal component analysis - p. 209 14. Chapter 13. Clustering analysis - p. 227 15. Part 3 - p. 247 16. Chapter 14. Dealing with missing data, small sample sizes, and heterogeneity in machine learning studies of brain disorders - p. 249 17. Chapter 15. Working with high-dimensional feature spaces: the example of voxel-wise encoding models - p. 267 18. Chapter 16. Multimodal integration - p. 283 19. Chapter 17. Bias, noise, and interpretability in machine learning: from measurements to features - p. 307 20. Chapter 18. Ethical issues in the application of machine learning to brain disorders - p. 329 21. Part 4 - p. 342 22. Chapter 19. A step-by-step tutorial on how to build a machine learning model - p. 343 23. Glossary - p. 371 24. Index - p. 379
Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners.
1. Chapter 1. Introduction to machine learning - p. 1 2. Chapter 2. Main concepts in machine learning - p. 21 3. Chapter 3. Applications of machine learning to brain disorders - p. 45 4. Part 2 - p. 65 5. Chapter 4. Linear regression - p. 67 6. Chapter 5. Linear methods for classification - p. 83 7. Chapter 6. Support vector machine - p. 101 8. Chapter 7. Support vector regression - p. 123 9. Chapter 8. Multiple kernel learning - p. 141 10. Chapter 9. Deep neural networks - p. 157 11. Chapter 10. Convolutional neural networks - p. 173 12. Chapter 11. Autoencoders - p. 193 13. Chapter 12. Principal component analysis - p. 209 14. Chapter 13. Clustering analysis - p. 227 15. Part 3 - p. 247 16. Chapter 14. Dealing with missing data, small sample sizes, and heterogeneity in machine learning studies of brain disorders - p. 249 17. Chapter 15. Working with high-dimensional feature spaces: the example of voxel-wise encoding models - p. 267 18. Chapter 16. Multimodal integration - p. 283 19. Chapter 17. Bias, noise, and interpretability in machine learning: from measurements to features - p. 307 20. Chapter 18. Ethical issues in the application of machine learning to brain disorders - p. 329 21. Part 4 - p. 342 22. Chapter 19. A step-by-step tutorial on how to build a machine learning model - p. 343 23. Glossary - p. 371 24. Index - p. 379
