

Everything you need to know about Machine Learning in 30 minutes or less - gmodena
http://www.hilarymason.com/presentations-2/devs-love-bacon-everything-you-need-to-know-about-machine-learning-in-30-minutes-or-less/?utm_source=twitterfeed&utm_medium=twitter

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monk_the_dog
If you already have a little exposure to machine learing, let me recomend an
interesting review paper [1] on random forests:
[http://research.microsoft.com/pubs/155552/decisionForests_MS...](http://research.microsoft.com/pubs/155552/decisionForests_MSR_TR_2011_114.pdf)

It isn't everything you need know in 30 minutes, but it's a concrete coverage
of lots of topics in machine learning in under 150 pages. Here's why I'm
recomending this paper:

* The algoritm is easy to understand.

* It can handle classification, regression, semi-supervised learning, manifold learning, and density estimation. The paper gives an introduction to each of these topics as well as a unified framework to implement each algorithm.

* It can handle categorical data and missing data [2]

* It gives as good results as other state of the art algorithms.

* The paper is well-written and easy to understand for someone without a deep background in machine learning.

[1] It's mostly a review paper. Using random forests for density estimation is
new.

[2] This review paper doesn't cover categorical data or missing data.

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droz
[http://lasa.epfl.ch/teaching/lectures/ML_Phd/Notes/ML_Lectur...](http://lasa.epfl.ch/teaching/lectures/ML_Phd/Notes/ML_Lecture_Notes_v2012.pdf)

Is another great resource that introduces many ML topics from the ground up.

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kqr2
Hilary Mason has a longer introduction to machine learning video using web
data, however, it isn't free.

<http://shop.oreilly.com/product/0636920017493.do>

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ramblerman
This looks really good. Thanks for the link

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marcelsalathe
Nice talk. The example of google translate is not a good one though. Say you
translate from language A to language B with 99% accuracy, and vice versa,
which would be pretty awesome, you'd still have a substantial quality decay
after only a few back and forth translations (0.99^x where x is the number of
translation steps).

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vecter
That's not a very realistic model of communication. If A communicates to B
with 99% accuracy in the text, it's likely that B will 100% understand what's
going. He will reply back to A with a 100% accurate message that's 99%
accurate after translation, and so forth.

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marcelsalathe
I agree. But my impression was that she took the fact that google translate
rapidly decays into gibberish as an indication that it's not doing a good
enough job. I don't think you can argue that exactly because google translate
does not have the interpretation capability.

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hmason
Don't read too much into that example -- I chose it as a humorous metaphor,
not a mathematical argument, and I messed up the delivery in the talk, anyway.

I'll refine the example for the next time!

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orenmazor
I'm really interested in doing more machine learning work (my current
projects, as interesting as they are, dont really require it).

I've done a few weirdo projects with NLTK, tho, and its great fun. By stream
hacking do you mean offloading learning sets (active or initial) and that
heavy overhead into the "cloud", or am I misunderstanding the terminology?

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hmason
In most data analysis work, we assume that the data resides in some database
and that you have the luxury of iterating over that data as many times as you
like to get to a final result.

The challenge with stream analysis is that you are dealing with a continuous
stream of data where you can see each element of the stream only one time and
must still be able to cluster/classify/analyze it. There are still few
algorithms and tools designed explicitly for that purpose.

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taliesinb
For more advanced audiences, here's a great resource I discovered recently:
<http://videolectures.net/mlss09uk_cambridge/> \-- 60 hours of lectures from
the giants of machine learning delivered at a summer school held at Cambridge
in 2009.

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jtagen
From the video, seems like this was a really tough crowd. Every other video
I've seen with her speaking had people laughing and enjoying themselves.

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geoffw8
Gutted. I was there on the Friday, but not the Saturday (which is when the
exciting stuff was). The BrewDog talk was best!

