
How to Machine Learn - gwulf
https://docs.google.com/document/d/1YN6BVdReNAYc8B0fjQ84yzDflqmeEPj7S0Xc-9_26R0/edit?usp=sharing
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raz32dust
Useful list. I would recommend adding one more resource for linear
algebra/machine learning:

I absolutely enjoyed learning Linear Algebra from these beautiful lectures by
Prof. Gilbert Strang (MIT):
[https://www.youtube.com/watch?v=ZK3O402wf1c&list=PLE7DDD9101...](https://www.youtube.com/watch?v=ZK3O402wf1c&list=PLE7DDD91010BC51F8)

Seriously, I gained a new found appreciation for Linear algebra after going
through these lectures. You should go over some of these lectures even if you
already know linear algebra - it might give you insights you never had before
(it did, for me). Absolute must-watch if you are into machine learning or
related areas.

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mattbettinson
I'm a freshman in university right now, how is linear algebra helpful in
computer science? I'm finding it hard to stay motivated as I can't think of
any uses outside of graphics. Maybe I'm just not far along in the course
though.

~~~
hiddencost
[http://math.stackexchange.com/questions/344879/how-does-
line...](http://math.stackexchange.com/questions/344879/how-does-linear-
algebra-help-with-computer-science)

Pretty much all of machine learning relies on linear algebra.

Most scientific computing relies on lI near algebra.

Graph algorithms often use linear algebra.

If you just want to be a Web Dev or an app developer, it probably doesn't
matter, tho.

But if you just want to do Web Dev or mobile apps then you don't really need
most of computer science.

~~~
fa
Well, if I remember my university's beginner linear algebra course, there were
many topics on the syllabus only due to historic accident, ancestor worship,
and theoretical necessities: I remember parallelepipeds, Cramer's rule,
solving eigensystems by solving for a polynomial's zeros... Let me tell you
how many times I've used parallelepipeds, Cramer's rule, or found eigenvalues
via the quadratic formula in the 12 years since linear algebra (and a career
in statistical signal processing and machine learning): zero, zero, and zero.
Most things in college are important only to the extent that they're
gatekeepers for what's really important. What hiddencost says is true, but
very little of what you're learning in class is relevant to those important
and interesting things. Sorry: college sucks.

~~~
karmacondon
Seconded. Same for discrete mathematics and differential equations.
Interesting to learn about, but pretty much worthless as soon as you set foot
off of campus. I'd love to see comments from anyone who has practically used
any of the information from those classes as a part of their daily duties as a
programmer of any kind.

~~~
fa
I watched Gilbert Strang's video lectures on linear algebra (the MIT freshman
course) for preparation for my PhD qualifier exam, and as a third year grad
student, I could appreciate the relevance of almost every single topic in the
class. That is, seven years after freshman linear algebra and with countless
applications programmed, papers read and implemented, and theoretical/applied
classes taken, it "all made sense" (don't ask me what a freshman is supposed
to make of that material, other than to acquire it at a very abstract
superficial level).

The early classes are the prerequisites for every and anything you might wind
up doing with math. Including becoming a math prof, or a web dev, or dropping
out. Nobody tells you, for every section of every textbook you have to read,
what its myriad applications might be, and you can't get a customized build of
just the topics you want.

But we're all startup people here right? Can this shortcoming be fixed? Can we
make a detailed dependency graph of topics in applied mathematics, which could
potentially be used to generate custom learning builds?

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anindyabd
The website for the project linked to on the first line is based on this
Bootstrap template: [http://startbootstrap.com/template-
overviews/grayscale/](http://startbootstrap.com/template-
overviews/grayscale/). At least they could have changed the background photo!

~~~
gwulf
Good find! The goal was to wrap the feature engineer scripts up with a front
end, so design was an after thought.

