Fundamentals of machine learning

Yan Liang; Jeong Yeol Yoon

doi:10.1016/B978-0-443-22001-2.00002-0

Fundamentals of machine learning

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

In this chapter, fundamental theories of machine learning (ML) will be presented. Unsupervised, supervised, versus reinforcement MLs will be explained. Various ML classification methods will be described that have been used for biosensing applications: naive Bayes classifier, logistic regression, decision tree, random forests, support vector machines, k-nearest neighbors, k-means clustering, XGBoost, and artificial neural network (ANN). Procedural examples will be presented using the scikit-learn library and Python coding.

Original language	English (US)
Title of host publication	Machine Learning and Artificial Intelligence in Chemical and Biological Sensing
Publisher	Elsevier
Pages	23-70
Number of pages	48
ISBN (Electronic)	9780443220012
ISBN (Print)	9780443220005
DOIs	https://doi.org/10.1016/B978-0-443-22001-2.00002-0
State	Published - Jan 1 2024
Externally published	Yes

Keywords

ANN
Bayes classifier
decision tree
k-NN
Machine learning
random forest
reinforcement ML
supervised ML
support vector machine
unsupervised RL
XGBoost

ASJC Scopus subject areas

General Chemistry

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10.1016/B978-0-443-22001-2.00002-0

Cite this

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abstract = "In this chapter, fundamental theories of machine learning (ML) will be presented. Unsupervised, supervised, versus reinforcement MLs will be explained. Various ML classification methods will be described that have been used for biosensing applications: naive Bayes classifier, logistic regression, decision tree, random forests, support vector machines, k-nearest neighbors, k-means clustering, XGBoost, and artificial neural network (ANN). Procedural examples will be presented using the scikit-learn library and Python coding.",

keywords = "ANN, Bayes classifier, decision tree, k-NN, Machine learning, random forest, reinforcement ML, supervised ML, support vector machine, unsupervised RL, XGBoost",

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KW - ANN

KW - Bayes classifier

KW - decision tree

KW - k-NN

KW - Machine learning

KW - random forest

KW - reinforcement ML

KW - supervised ML

KW - support vector machine

KW - unsupervised RL

KW - XGBoost

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BT - Machine Learning and Artificial Intelligence in Chemical and Biological Sensing

PB - Elsevier

ER -

Fundamentals of machine learning

Abstract

Keywords

ASJC Scopus subject areas

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