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Full Description
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.
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
