Machine Learning - 1st Edition - ISBN: 9780128157398

Machine Learning

1st Edition

Methods and Applications to Brain Disorders

Editors: Andrea Mechelli Sandra Vieira
Paperback ISBN: 9780128157398
Imprint: Academic Press
Published Date: 1st November 2019
Page Count: 400
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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.

Key Features

  • 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.

Readership

Advanced students and researchers in behavioral neuroscience, psychology, psychiatry, psychology and neurology

Table of Contents

Part I

Chapter 1: Introduction to machine learning

Chapter 2: Main concepts in machine learning

Chapter 3: Applications of machine learning to brain disorders

 

Part II

Chapter 4: Linear regression

Chapter 5: Linear methods for classification

Chapter 6: Support vector machine

Chapter 7: Support vector regression

Chapter 8: Multiple kernel learning

Chapter 9: Deep neural networks

Chapter 10: Convolutional neural networks

Chapter 11: Autoencoders

Chapter 12: Principal component analysis

Chapter 13: K-means clustering

 

Part III

Chapter 14: Dealing with missing data, small sample sizes, and heterogeneity

Chapter 15: Working with high dimensional feature spaces: the example of voxel-wise encoding models

Chapter 16: Multimodal integration

Chapter 17: Bias, noise and interpretability in machine learning: from measurements to features

Chapter 18: Ethical issues in the application of machine learning to brain disorders

 

Part IV

Chapter 19: A step-by-step tutorial on how to build a machine learning model 

Details

No. of pages:
400
Language:
English
Copyright:
© Academic Press 2020
Published:
Imprint:
Academic Press
Paperback ISBN:
9780128157398

About the Editor

Andrea Mechelli

Andrea Mechelli is a clinical psychologist and a neuroscientist with an interest in the early detection and treatment of mental illness. After studying Psychology at the University of Padua (1999), he completed a PhD in Neurological Sciences at University College London in 2002 and became an academic member of staff at King's College London in 2004. He currently holds the position of Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience at King's College London. Prof. Mechelli's research involves the application of advanced machine learning methods to clinical, neuroimaging and smartphone data, with the aim of developing and validating novel tools for early detection and treatment.

Affiliations and Expertise

Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom

Sandra Vieira

Sandra Vieira is a researcher at the Institute of Psychiatry at King’s College London. She received her first Masters from Clinical Psychology in 2011 from the University of Coimbra, Portugal, and her second Masters in Psychiatric Research in 2014 from King’s College London. United Kingdom. She is currently completing her PhD in Psychosis Studies at the Institute of Psychiatry, Psychology & Neuroscience (King’s College London). Her research focuses on the application of advanced machine learning methods to investigate brain abnormalities in psychosis.

Affiliations and Expertise

Researcher at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom.

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