Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by

Description: Introduction to Applied Linear Algebra by Stephen Boyd, Lieven Vandenberghe A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Author Biography Stephen Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering at Stanford University,California, with courtesy appointments in the Department of Computer Science, and the Department of Management Science and Engineering. He is the co-author of Convex Optimization (Cambridge, 2004), written with Lieven Vandenberghe. Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles, with a joint appointment in the Department of Mathematics. He is the co-author, with Stephen Boyd, of Convex Optimization (Cambridge, 2004). 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. Review 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, BerkeleyThis book explains the least squares method and the linear algebra it depends on - and the authors do it right! Gilbert Strang, Massachusetts Institute of TechnologyThe 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, CaliforniaBoyd 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… this book … could be used either as the textbook for a first course in applied linear algebra for data science or (using the first half of the book to review linear algebra basics) the textbook for a course in linear algebra for data science that builds on a prior to introduction to linear algebra … This is a very well written textbook that features significant mathematics, algorithms, and applications. I recommend it highly. Brian Borchers, MAA Reviews Promotional A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples. Review Quote Advance praise: 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 Promotional "Headline" A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples. Description for Bookstore A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance. Description for Library A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance. Details ISBN1316518965 Author Lieven Vandenberghe Publisher Cambridge University Press Year 2018 ISBN-10 1316518965 ISBN-13 9781316518960 Format Hardcover Imprint Cambridge University Press Place of Publication Cambridge Country of Publication United Kingdom Affiliation University of California, Los Angeles Subtitle Vectors, Matrices, and Least Squares DEWEY 512.5 Media Book Publication Date 2018-06-07 Pages 474 Short Title Introduction to Applied Linear Algebra Language English UK Release Date 2018-06-07 AU Release Date 2018-06-07 NZ Release Date 2018-06-07 Illustrations Worked examples or Exercises Alternative 9781108583664 Audience Tertiary & Higher Education We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:168625216;

Price: 103.87 AUD

Location: Melbourne

End Time: 2025-01-23T02:25:42.000Z

Shipping Cost: 18.4 AUD