Machine Learning for Engineers
- Author: Osvaldo Simeone, King's College London
- Date Published: November 2022
- availability: Available
- format: Hardback
- isbn: 9781316512821
Hardback
-
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: an 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; 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.
Read more- A book on machine learning written for engineers, by an engineer
- An accessible text with a unified information-theoretic framework
- Highlights connections between machine learning and estimation, detection, information theory, and optimization
- Offers concise but extensive coverage of state-of-the-art topics with simple, reproducible examples
- Derives modern methods, such as generative adversarial networks, from first principles, revealing their connection with standard techniques
- Divided into useful parts, allowing the book easily to be mapped to either a one- or a two-semester course
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: November 2022
- format: Hardback
- isbn: 9781316512821
- length: 450 pages
- dimensions: 261 x 209 x 38 mm
- weight: 1.47kg
- availability: Available
Table of Contents
Part I. Introduction and Background:
1. When and how to use machine learning
2. Background. Part II. Fundamental Concepts and Algorithms:
3. Inference, or model-driven prediction
4. Supervised learning: getting started
5. Optimization for machine learning
6. Supervised learning: beyond least squares
7: Unsupervised learning. Part III. Advanced Tools and Algorithms:
8. Statistical learning theory
9. Exponential family of distributions
10. Variational inference and variational expectation maximization
11. Information-theoretic inference and learning
12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning:
13. Transfer learning, multi-task learning, continual learning, and meta-learning
14. Federated learning. Part V. Epilogue:
15. Beyond this book.-
Resources for
Machine Learning for Engineers
Osvaldo Simeone
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact [email protected].
Related Books
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email [email protected]
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×