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> Machine Learning

Machine Learning A First Course for Engineers and Scientists


Andreas Lindholm, Annotell, Sweden, Niklas Wahlström, Uppsala Universitet, Sweden, Fredrik Lindsten, Linköpings Universitet, Sweden, Thomas B. Schön, Uppsala Universitet, Sweden
Published 2022


This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all…

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Key features

  • The coherent statistical framework highlights the similarities and differences between the different methods
  • Introduces each new method in the simplest possible meaningful, setting before presenting the general idea
  • Revisits the same data set several times throughout the book

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