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.
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Table of Contents
Part I 1. Introduction to machine learning 2. Main concepts in machine learning 3. Applications of machine learning to brain disorders
Part II 4. Linear regression 5. Linear methods for classification 6. Support vector machine 7. Support vector regression 8. Multiple kernel learning 9. Deep neural networks 10. Convolutional neural networks 11. Autoencoders 12. Principal component analysis 13. K-means clustering
Part III 14. Dealing with missing data, small sample sizes, and heterogeneity 15. Working with high dimensional feature spaces: the example of voxel-wise encoding models 16. Multimodal integration 17. Bias, noise and interpretability in machine learning: from measurements to features 18. Ethical issues in the application of machine learning to brain disorders
Part IV 19. A step-by-step tutorial on how to build a machine learning model
