'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

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