other · 2024

Machine Learning Algorithms

Cited by 3Abstract and details only

Abstract

This book chapter covers machine learning algorithms, including linear regression, logistic regression, support vector machines (SVM), artificial neural networks (ANN), and unsupervised machine learning algorithms. It explains the mathematics behind linear regression and gradient descent optimization. Implementation details are provided for univariate and multiple linear regression. Logistic regression is discussed for both binary and multinomial cases, along with practical applications. SVM is explored for linearly separable and nonlinear data, as well as regression tasks. ANN is introduced with multilayer perceptrons (MLP), including parameter estimation and backpropagation. CNNs and RNNs, including LSTM, are discussed, along with their applications. Unsupervised learning algorithms such as k-means, mean shift, and DBSCAN are covered. The chapter concludes by introducing the application of machine learning algorithms with Hephaistos. This chapter provides a comprehensive overview of machine learning algorithms and their applications.

Cite this paper

APA 7

Vasques, X. (2024). Machine Learning Algorithms. https://doi.org/10.1002/9781394220649.ch3

MLA 9

Vasques, Xavier. “Machine Learning Algorithms.” 2024, pp. 175–272, https://doi.org/10.1002/9781394220649.ch3.

Chicago

Vasques, Xavier. 2024. “Machine Learning Algorithms.” https://doi.org/10.1002/9781394220649.ch3.

Harvard

Vasques, X. (2024) ‘Machine Learning Algorithms’. Available at: https://doi.org/10.1002/9781394220649.ch3.

BibTeX

@misc{vasques2024machine,
  title = {{Machine Learning Algorithms}},
  author = {Vasques, Xavier},
  pages = {175--272},
  year = {2024},
  doi = {10.1002/9781394220649.ch3}
}

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