ML Book

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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, and neurology

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

  1. Linear regression

  2. Linear methods for classification

  3. Support vector machine

  4. Support vector regression

  5. Multiple kernel learning

  6. Deep neural networks

  7. Convolutional neural networks

  8. Autoencoders

  9. Principal component analysis

  10. K-means clustering

Part III

  1. Dealing with missing data, small sample sizes, and heterogeneity

  2. Working with high dimensional feature spaces: the example of voxel-wise encoding models

  3. Multimodal integration

  4. Bias, noise and interpretability in machine learning: from measurements to features

  5. Ethical issues in the application of machine learning to brain disorders

Part IV

  1. A step-by-step tutorial on how to build a machine learning model

Python code Access the python codes from Chapter 19 in this link.