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.
Provides a non-technical introduction to machine learning and applications to brain disorders
Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches
Covers the main methodological challenges in the application of machine learning to brain disorders
Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python
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