Machine Learning: A First Course for Engineers and Scientists (Foundations And Trends In Machine Learning Ser.)

Synopsis

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.

Copyright:

2019

Book Details

Book Quality:

Publisher Quality

Book Size:

158 Pages

ISBN-13:

9781108911498

Related ISBNs:

9781108843607

Publisher:

Cambridge University Press

Date of Addition:

03/31/22

Copyrighted By:

Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten & Thomas B. Schön

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.