Numerical Methods for Engineering and Data Science
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 22 May 2025
- ISBN 9781032200699
- Binding Hardback
- No. of pages478 pages
- Size 229x152 mm
- Language English
- Illustrations 116 Illustrations, black & white; 1 Halftones, black & white; 115 Line drawings, black & white; 13 Tables, black & white 700
Categories
Short description:
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.
MoreLong description:
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 on the theoretical underpinnings, with an 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.
MoreTable of Contents:
1. Introduction Part I ? Numerical Methods for Engineering Applications 2. Numerical Errors 3. Solving Algebraic Equations 4. Systems of Linear Equations 5. Orthogonality 6 Linear Least Square Regression 7. Polynomial Interpolation 8. Numerical Integration 9. Initial Value Problems Part II ? Numerical Methods for Data Analysis 10. Machine Learning 11. Regression Models 12. Model Selection 13. Classification 14. Tree-Based Algorithms
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