
Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares [pdf] - Anon84
https://web.stanford.edu/~boyd/vmls/vmls.pdf
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imurray
Please don't deep link to PDFs when there is a sensible contents page that has
more information and other links.

[https://web.stanford.edu/~boyd/vmls/](https://web.stanford.edu/~boyd/vmls/)

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sannee
As a rule-of-thumb, I would stay away from any "linear algebra" textbook that
does not even have definition of an abstract vector space in the first few
chapters. In a sense, it's like programming without data types.

When I was in high school, I learned linear algebra from similar textbook, but
it wasn't until college (and that was an engineering linear algebra course!)
that I got any sort of understanding of what it's really about.

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scoobyyabbadoo
Interesting how k-means clustering is considered appropriate for chapter 4 of
this 19 chapter book. I don't know anyone who even considers it related to the
material in linear algebra (which is more about vectors, matrices, spectral
values, and applications, whereas k-means is more an application of knowledge
about algorithms and data structures). But I assume the pressure to sneak ML
topics into dry fundamentals courses is inevitable.

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graycat
K-means was in optimization in operations research decades before anyone heard
of _machine learning_ in computer science.

 _Computer science_ was not a prerequisite to that work in optimization.

Optimization in operations research and much of applied math more generally
are awash in important algorithms without reference to computer science and,
really, e.g., the Dantzig simplex algorithm of about 1949, before computer
science was a recognized academic field.

Such applied math includes A. Wald's work on _sequential testing_ in
statistics, the fast Fourier transform, Gram-Schmidt orthogonalization, Gauss
elimination, Gauss-Siedel iteration, the Hungarian method for maximum
matching, Runge-Kutta in ordinary differential equations, and much more.

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scoobyyabbadoo
OK, but why is it in a book on Linear Algebra today? Look at any introductory
Linear Algebra book more than 3 years old and you won't find it in the
curriculum

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jazzyjackson
Do you have a personal vendetta against clustering or something?

I don't know too much about linear algebra but I know I had to learn some when
my boss wanted me to do unsupervised classification. Maybe the book just
considers it a good application of the material.

What do old books have to do with what should be included in new books?

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scoobyyabbadoo
ML is hyped enough already and kids already don't know enough fundamentals.
They're never going to sit in front of a Linear Algebra class again so the
appropriate use of their time is to teach them as much linear algebra as
possible and not to turn it into a class on today's hype train

