BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Machine Learning: A First Course for Engineers and Scientists

Machine Learning: A First Course for Engineers and Scientists

Machine Learning: A First Course for Engineers and Scientists
Stock photo: cover may vary

Machine Learning: A First Course for Engineers and Scientists

Add to wish list
  • New
New

Description

New/New. Brand New Original US Edition, Perfect Condition. Printed in English. Excellent Quality, Service and customer satisfaction guaranteed!
Ask the seller a question Add to wish list
A$90.56
A$22.01 Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Ships from Students Textbooks (India)

Details

  • Title Machine Learning: A First Course for Engineers and Scientists
  • Condition New
  • Features Bibliography, Index
  • Bookseller's Inventory # BIBNNA-151896
  • ISBN 9781108843607
  • Quantity available 1

About Students Textbooks India

Biblio member since 2009

Selling textbooks, International editions and reference books online from last 5 Years.

Terms of Sale:

30 day return guarantee, with full refund including shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged. Return address: Students_Textbooks 12 phankha road Jankpuri New Delhi 110036 India

Browse books from Students Textbooks

Reader reviews for Machine Learning: A First Course for Engineers and Scientists

From the publisher

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.
tracking-