Titlebook: Machine Learning for Engineers; Using data to solve Ryan G. McClarren Textbook 2021 Springer Nature Switzerland AG 2021 supervised learnin

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https://doi.org/10.1007/978-3-030-70388-2supervised learning; unsupervised learning; Bayesian statistics; linear models; tree-based models; deep n

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978-3-030-70390-5Springer Nature Switzerland AG 2021

Consequence

Textbook 2021merging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates th

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Recurrent Neural Networks for Time Series Dataut sequences are long. We then develop a more sophisticated network, the long short-term memory (LSTM) network to deal with longer sequences of data. Examples include predicting the frequency and shift of a signal and predicting the behavior of a cart-mounted pendulum

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Finding Structure Within a Data Set: Data Reduction and Clustering clusters in the data set are found using distance measures in the independent variables, and t-SNE, where high-dimensional data are mapped into a low-dimensional (2 or 3 dimensions) data set to visualize the clusters. We close this chapter by applying supervised learning methods to hyper-spectral imaging of plant leaves.