Name: Machine Learning: A First Course for Engineers and Scientists (en Inglés)
Price: 91830.00 CLP
Availability: InStock
Author: Lindholm, Andreas ; Wahlström, Niklas ; Lindsten, Fredrik
ISBN: 9781108843607

portada Machine Learning: A First Course for Engineers and Scientists (en Inglés)

Formato

Libro Físico

Autor

Andreas Lindholm

Niklas Wahlström

Fredrik Lindsten

Editorial

Cambridge University Press

Año

2022

Idioma

Inglés

N° páginas

325

Encuadernación

Tapa Dura

Dimensiones

25.7 x 18.3 x 2.0 cm

Peso

0.86 kg.

ISBN13

9781108843607

Categorías

Reconocimiento de formas
Visión por ordenador

Machine Learning: A First Course for Engineers and Scientists (en Inglés)

Andreas Lindholm (Autor) · Niklas Wahlström (Autor) · Fredrik Lindsten (Autor) · Cambridge University Press · Tapa Dura

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Reseña del libro "Machine Learning: A First Course for Engineers and Scientists (en Inglés)"

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 methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.