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

by Stephen Boyd, Lieven Vandenberghe

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.

Genres: Mathematics, Science, Algebra, Programming, Nonfiction, Computer Science, Reference

474 pages, Hardcover

Published August 23, 2018

Reviews

[Alexander Temerev · Rating 4 out of 5]

An excellent, well-written introduction to linear algebra for software engineers and (novice) data scientists; notable for its focus on practical problems in regression, classification and optimal control. I definitely recommend it to everyone interested.

[Igor Fomenko · Rating 5 out of 5]

This book doesn't contain info about Gauss elimination, determinant, eigenvals and eigenvecs, rank , conditional number, lu and svd decomposion, you don't get info about how many solutions system has but...
only first 200 pages of this book gave me ability to write my own 3d geometric solver. It was so easy and amazing! I had a lot of questions after reading it and I found answers in another book, but I'm happy that this one was my first linear algebra book.

[Sivakumar Balasubramanian · Rating 5 out of 5]

An accessible book to get a basic flavor for matrix theory and its most common/important application. I thoroughly enjoyed reading the book. The numerous examples showcasing the different applications of ideas covered in the book make it really easy for a newcomer to see the value of matrix theory.

[Amir V · Rating 4 out of 5]

Pros: Excellent problem set and the myriad examples of how linear algebra techniques can be applied to a variety of problems. The emphasis on the connections between linear algebra theory and problems such as KNN clustering (and also pointing out the fascinating 'approximate factorization' perspective on KNN) and regularized regression was the best selling point of the book for me.


Cons: A bit lacking in visualizations and (language) simplicity, so not my first suggestion as a 'first' linear algebra book. Some chapters are much better after some knowledge in (vector) calculus. Matter of taste, but a bit too much reliance on econometrics problems.

[Saeed Najafi · Rating 2 out of 5]

The book just gives a naive introduction to linear algebra.

[Lars Folkerts · Rating 3 out of 5]

I wanted a relearn linear algebra before I started machine learning. I found this book assessible compared to most linear algebra books, but felt the author spent too much time on explaining applications. I also thought the book could use more direct practice problems with odd njmber solutions in the back. I was also dissappointed that Eigenvalue concepts were not in the book.

(I used Khan Academy after this book, which helped my learning goals)

[Asher · Rating 4 out of 5]

Very introductory, very applied - exactly what’s on the cover. Might be a bit too light on theory for engineering students