Numerical Methods for Engineering and Data Science

Name: Numerical Methods for Engineering and Data Science
Brand: Taylor & Francis
Price: 62.69 AUD
Availability: PreOrder

By: Rolf Wuthrich, Carole El Ayoubi

Write A Review

eText | 22 May 2025 | Edition Number 1

At a Glance

Format
ePUB

eText

$62.69

or 4 interest-free payments of $15.67 with

Available: 22nd May 2025

Preorder. Online access available after release.

Read online on

Desktop

Tablet

Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Numerical Methods for Engineering and Data Science guides students in implementing numerical methods in engineering and in assessing their limitations and accuracy, particularly using algorithms from the field of machine learning.

The textbook presents key principles building upon the fundamentals of engineering mathematics. It explores classical techniques for solving linear and nonlinear equations, computing definite integrals, and differential equations. Emphasis is placed both on the theoretical underpinnings, with in depth discussion of the sources of errors, and in the practical implementation of these using Octave. Each chapter is supplemented with examples and exercises designed to reinforce the concepts and encourage hands-on practice. The second half of the book transitions into the realm of machine learning. The authors introduce basic concepts and algorithms, such as linear regression and classification. As in the first part of this book, a special focus is on the solid understanding of errors and practical implementation of the algorithms. In particular the concepts of bias, variance and noise are discussed in detail and illustrated with numerous examples.

This book will be of interest to students in all areas of engineering, alongside mathematicians and scientists in industry looking to improve their knowledge of this important field.