Machine Learning A First Course for Engineers and Scientists
- Textbook
Description
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
About the book
- DOI https://doi.org/10.1017/9781108919371
- Subjects Communications and Signal Processing,Computer Science,Engineering,Machine Learning and Pattern Recognition
- Format: Hardback
- Publication date: 02 June 2022
- ISBN: 9781108843607
- Dimensions (mm): 253 x 177 mm
- Weight: 0.88kg
- Page extent: 350 pages
- Availability: In stock
- Format: Digital
- Publication date: 27 May 2022
- ISBN: 9781108919371
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Online publication date: 23 September 2020