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Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares Hardcover – 23 Aug 2018

4.7 out of 5 stars 8 customer reviews
ISBN-13: 978-1316518960 ISBN-10: 1316518965 Edition: 1st

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'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

'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

'… 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

Book Description

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.

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Most helpful customer reviews on Amazon.com

Amazon.com: 4.7 out of 5 stars 8 reviews
Layton Baker
5.0 out of 5 starsFantastic introduction to numerical linear algebra
20 January 2019 - Published on Amazon.com
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22 people found this helpful
Alex N
4.0 out of 5 starsIt's an interesting approach to study linear algebra
23 March 2019 - Published on Amazon.com
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12 people found this helpful
Igor Kleiner
5.0 out of 5 starsPerfect introduction in applied linear algebra and optimization
11 February 2019 - Published on Amazon.com
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5 people found this helpful
Praveen Joseph
5.0 out of 5 starsExcellent introduction to Linear Algaebra
13 April 2019 - Published on Amazon.com
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2 people found this helpful
Kirill Chilingarashvili
4.0 out of 5 starsHigh quality print, great content, no solutions
2 July 2019 - Published on Amazon.com
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One person found this helpful