

Machine Learning - Introduction - phenylene
http://jeremykun.wordpress.com/2012/08/04/machine-learning-introduction/

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rdudekul
"It is this author’s personal belief that the most important part of machine
learning is the mathematical foundation, followed closely by efficiency in
implementation details."

An introductory ML material in my opinion can be less mathematically rigorous.
Emphasis can be on intuitive understanding of principles of various
techniques, the strengths and weaknesses of each and the application of ML
techniques to various simplified problems for practice. It is easy to get lost
in too much Math and loose sight of real world problem solving.

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rm999
I've interviewed several people for machine learning/data science positions
and I've found when people don't get the math behind machine learning they
don't get the machine learning. Math, specifically linear algebra, is the
language that lets you move from our 2/3 dimensional thinking to a more
abstract high-dimensional space: the one machine learning lives in. It's easy
to draw a bunch of points on a piece of paper and then draw a line between it
and say "this is linear regression!" It's much harder to argue why
regularization is important and why/how you would want to use/tweak it. The
math is essential to getting important aspects of machine learning like this.

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Homunculiheaded
Although I think there are degrees of mathematical understanding in ML, and
I've noticed people often mean very different things when they make statements
like "less mathematically rigorous". Understanding how/why regularization
works is pretty trivial mathematics and if you don't understand how that works
I'd agree that ML is a bit too 'black box'. But look at something like the
Kernel trick in SVMs. I'd argue it's important to understand the idea of
mapping points in one dimensionality to another in order to understand why you
would use a linear vs Gaussian kernel. However the mathematics required to
create your own kernel functions is much less trivial. If you're going to be
doing original research is SVMs I would say this is required math, but for
practical ML knowledge of 'how' a kernel behaves without a deep understanding
of 'why' would be adequate. I would consider an understanding of how but not
why to be 'less mathematically rigorous'

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dave_sullivan
Cool, I'm excited to read this series.

Was a little disappointed to see neural networks noted as "classical" with
SVMs designated "modern". And nothing about deep learning? Autoencoders? How
about different optimization methods--Truncated newton vs gradient descent?

Some of the most interesting recent developments in ML seem to be left out,
even if it is just an introduction.

