This groundbreaking textbook is a combination of straightforward explanations with a wealth of practical examples in order to offer an innovative approach to teaching linear algebra.

This book is meant to provide an introduction to vectors, matrices, least squares methods as well as basic topics in applied linear algebra. The main goal of the book is to give the beginning student, with little or no prior exposure to linear algebra, a good grounding in the basic ideas, as well as an appreciation for how they are used in many applications, including data fitting, machine learning and artificial intelligence, finance, tomography, navigation, image processing and automatic control systems.

Does this book require any prior knowledge of the field?

Requiring no prior knowledge of the subject, the book 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 inclusion of numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems. 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.

The main goal of the book:

The book, as already mentioned before does not require any knowledge of computer programming and can be used as a conventional textbook, by reading the chapters and working the exercises that do not involve numerical computation.

This approach, however, misses out on one of the most compelling reasons to learn the material: You can use the ideas and methods described in this book to do practical things like building a prediction model from data, enhance images, or optimize an investment portfolio.

The growing power of computers, together with the development of high-level computer languages and packages that support vector and matrix computation, have made it easy to use the methods described in this book for real applications.

For this reason, it is hoped that every student of this book will complement their study with computer programming exercises and projects, including some that involve real data

About the author:

Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering at Stanford University with courtesy appointments in the Department of Computer Science, and the Department of Management Science and Engineering.

Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department at UCLA, with a joint appointment in the Department of Mathematics. He is the co-author, with Stephen Boyd, of Convex Optimization (Cambridge, 2004).

Source and link to the PDF: Click Here