Introduction to machine learning

TY - CHAP

T1 - Introduction to machine learning

AU - Vieira, Sandra

AU - Lopez Pinaya, Walter Hugo

AU - Mechelli, Andrea

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Machine learning is becoming increasingly popular in the neuroscientific literature. However, navigating the literature can easily become overwhelming, especially for the nonexpert. In this chapter, we provide an introduction to machine learning aimed at researchers, clinicians, and students with an interest in brain disorders, including psychiatry and neurology. We first provide a brief overview of how the most prominent theories of human learning from the fields of psychology and neuroscience influenced the development of modern cutting-edge machine learning methods. Second, we discuss how these methods differ from classical statistics and why they could be particularly suited to the investigation of brain disorders. In the final section of this chapter, we introduce a high-level taxonomy of the main approaches used in the machine learning literature: supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning.

AB - Machine learning is becoming increasingly popular in the neuroscientific literature. However, navigating the literature can easily become overwhelming, especially for the nonexpert. In this chapter, we provide an introduction to machine learning aimed at researchers, clinicians, and students with an interest in brain disorders, including psychiatry and neurology. We first provide a brief overview of how the most prominent theories of human learning from the fields of psychology and neuroscience influenced the development of modern cutting-edge machine learning methods. Second, we discuss how these methods differ from classical statistics and why they could be particularly suited to the investigation of brain disorders. In the final section of this chapter, we introduce a high-level taxonomy of the main approaches used in the machine learning literature: supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning.

KW - Brain disorders

KW - Classical statistics

KW - Generalization

KW - Heterogeneity

KW - Human learning

KW - Machine learning

KW - Neuroscience

KW - Prediction

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85079506486&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-815739-8.00001-8

DO - 10.1016/B978-0-12-815739-8.00001-8

M3 - Chapter

SN - 9780128157398

SP - 1

EP - 20

BT - Machine Learning

PB - Elsevier

ER -