Machine learning for engineers /
This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between e...
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge ; New York :
Cambridge University Press,
[2023].
|
| Subjects: |
| Summary: | This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory and optimization, it includes accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study, clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices, demonstration of the links between information-theoretical concepts and their practical engineering relevance, and reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines. |
|---|---|
| Physical Description: | xxii, 578 pages : illustrations ; 26 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781316512821 1316512827 |