Introduction to Applied Linear Algebra
Vectors, Matrices, and Least Squares
£35.99
- Authors:
- Stephen Boyd, Stanford University, California
- Lieven Vandenberghe, University of California, Los Angeles
- Date Published: June 2018
- availability: In stock
- format: Hardback
- isbn: 9781316518960
£
35.99
Hardback
-
This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.
Read more- Shows students how a few fundamental linear algebra concepts and techniques underlie a wide variety of applications
- Provides a revolutionary new approach to teaching linear algebra methods to aspiring data scientists
- Includes numerous practical examples and exercises, allowing students to translate their knowledge of abstract linear algebra into real-world applications
Reviews & endorsements
'Introduction to Applied Linear Algebra fills a very important role that has been sorely missed so far in the plethora of other textbooks on the topic, which are filled with discussions of nullspaces, rank, complex eigenvalues and other concepts, and by way of 'examples', typically show toy problems. In contrast, this unique book focuses on two concepts only, linear independence and QR factorization, and instead insists on the crucial activity of modeling, showing via many well-thought out practical examples how a deceptively simple method such as least-squares is really empowering. A must-read introduction for any student in data science, and beyond!' Laurent El Ghaoui, University of California, Berkeley
See more reviews'This book explains the least squares method and the linear algebra it depends on - and the authors do it right!' Gilbert Strang, Massachusetts Institute of Technology
'The kings of convex optimization have crossed the quad and produced a wonderful fresh look at linear models for data science. While for statisticians the notation is a bit quirky at times, the treatise is fresh with great examples from many fields, new ideas such as random featurization, and variations on classical approaches in statistics. With tons of exercises, this book is bound to be popular in the classroom.' Trevor Hastie, Stanford University, California
'Boyd and Vandenberghe present complex ideas with a beautiful simplicity, but beware! These are very powerful techniques! And so easy to use that your students and colleagues may abandon older methods. Caveat lector!' Robert Proctor, Stanford University, California
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: June 2018
- format: Hardback
- isbn: 9781316518960
- length: 474 pages
- dimensions: 253 x 195 x 25 mm
- weight: 1.18kg
- availability: In stock
Table of Contents
Part I. Vectors:
1. Vectors
2. Linear functions
3. Norm and distance
4. Clustering
5. Linear independence
Part II. Matrices:
6. Matrices
7. Matrix examples
8. Linear equations
9. Linear dynamical systems
10. Matrix multiplication
11. Matrix inverses
Part III. Least Squares:
12. Least squares
13. Least squares data fitting
14. Least squares classification
15. Multi-objective least squares
16. Constrained least squares
17. Constrained least squares applications
18. Nonlinear least squares
19. Constrained nonlinear least squares
Appendix A
Appendix B
Appendix C
Appendix D
Index.-
Resources for
Introduction to Applied Linear Algebra
Stephen Boyd, Lieven Vandenberghe
Lecturer Resources
Welcome to the resources site
Here you will find free-of-charge online materials to accompany this book. The range of materials we provide across our academic and higher education titles are an integral part of the book package whether you are a student, instructor, researcher or professional.
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
*This title has one or more locked files and access is given only to lecturers adopting the textbook for their class. We need to enforce this strictly so that solutions are not made available to students. To gain access to locked resources you either need first to sign in or register for an account.
These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.
If you are having problems accessing these resources please email lecturers@cambridge.org
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 lecturers@cambridge.org
Register Sign in» Proceed