

A few useful things to know about machine learning (2012) [pdf] - chaitanyav
http://www.cs.washington.edu/homes/pedrod/papers/cacm12.pdf

======
AndrewOMartin
There are a few criticisms that can be made of this paper, it tries to cover a
lot of ground in a small space, has rather informal language, and possibly
more, but these can be forgiven as it's a generally informative and
entertaining piece.

For me, the largest omission is the lack of reference to theoretical limit of
Machine Learning. That is, what can't be achieved even if you assume infinite
resources and algorithmic complexity. It's important for me as this paper
appears to be a damn good stab at being a comprehensive review of why machine
learning projects fail, except for missing this critical point. The idea is
best explored in the book What Computers Can't Do (H.Dreyfus, 1972), recounted
in the book What Computer's Still Can't Do (H.Dreyfus, 1992), and well
summarized in A History of First Step Fallacies (H.Dreyfus, 2012) [1]

Finally, any paper that's freely distributed, can be enjoyed over lunch and
includes the phrase "most of the volume of a high-dimensional orange is in the
skin, not the pulp" is fine in my book.

[1] -
[http://link.springer.com/article/10.1007%2Fs11023-012-9276-0](http://link.springer.com/article/10.1007%2Fs11023-012-9276-0)
[PDF]

~~~
Patient0
The abstract was intriguing. It's a shame you have to pay to read the rest of
the paper.

------
abrichr
_8\. FEATURE ENGINEERING IS THE KEY_

 _Feature engineering is more difficult because it 's domain-specific, while
learners can be largely general-purpose ... one of the holy grails of machine
learning is to automate more and more of the feature engineering process._

This is the goal of deep learning, and more generally, representation
learning: automatic discovery of explanatory features from large amounts of
data. I'm surprised it wasn't mentioned.

~~~
blutoot
This is a paper published in 2012. Deep learning wasn't mainstream at the time
of writing it (probably 2011-2012).

~~~
mjw
Machine learning has been full of methods for learning latent feature
representations since way before deep learning was trendy, from simple things
like PCA to more sophisticated Bayesian models. Deep learning refers
specifically to using multi layer neural models and while neat is only one way
of doing it and certainly didn't invent the whole concept of learning feature
representations as recently as 2012!

