
Learning About Machine Learning 2nd Ed. - liebke
http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html
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hanskuder
For good lectures and slides, see Andrew Moore's collection on statistical
data mining:

<http://www.autonlab.org/tutorials/>

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khelloworld
I'm a noob and so here is my question: What will I be able to create after I
go through all the books mentioned on the list?

I ask this question keeping the current state of AI in mind.

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oakenshield
IMO, AI is logically quite different from Machine Learning / Statistical
Learning.

The stuff mentioned on this page is largely about methods to learn from
structured or unstructured data, and this is a field that has become highly
relevant of late due to the data deluge. Research in these areas has
progressed immensely as well, and we now have methods to mine many different
types and volumes of data. If you have a good grip of statistical techniques
and some basic ML ideas, you will be able to single out and pick the right
technique that fits your problem, given your data type, SNR ratio, structured-
ness, volume, your resource constraints, etc. Knowing a little more about ML
will also allow you to change/invent new methods to suit your own problems
better (e.g., a new way to compress your feature space).

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ogrisel
I mostly agree with parent, but the divorce between AI and ML might only be
temporary, and the deep learning branch of ML is targeting more general AI
than just fitting linear models, c.f. for instance for a high level overview:

    
    
      http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf

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icey
I missed the first one of these when it was originally posted, but it's a huge
thread full of really interesting material:

<http://news.ycombinator.com/item?id=1055042>

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Estragon
Seconding his recommendation of _Probability Theory: The Logic of Science_

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ableal
This leads to a nice summary that may also interest the same audience (posted
by fogus, seems to be slipping away ...):

<http://news.ycombinator.com/item?id=1187148>

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agentq
Baye Sean, relative of Jay Sean? :)

This is a great list, I'd also recommend Ross's books on probability as
starting points.

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bradfordcross
Nice catch.

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tcc619
Great book list. I strongly recommend getting a strong gasp of linear algebra
as matrices are the a great way to think of large data in a manageable way.

also, for folks who just want to their feet wet, oreilly's programming
collective intelligence is a good start.

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bradfordcross
"for folks who just want to their feet wet, oreilly's programming collective
intelligence is a good start."

No, it is not a good start.

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tcc619
can you explain why not?

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plinkplonk
"can you explain why not?"

see <http://news.ycombinator.com/item?id=208811>

I said there "PCI takes (in my opinion, feel free to differ) a math-lite,
"dummies guide" approach to AI algorithms. "

Brad's approach and recommendations are in the opposite direction.

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physcab
I'm just curious. Have you read most of these books or did you get these
recommendations from people you've networked with?

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bradfordcross
I own them all, and I am perpetually at different stages of working through
each of them. :-) I work in the way I explained in the intro - when I don't
know something, I step back and go learn the background I need to move
forward.

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abossy
Do you find your time spent committed to learning conflicting with your time
spend doing product development for Flightcaster?

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bradfordcross
On the contrary; I find learning and production to be mutually self-
reinforcing rather than conflicting.

I am a professional committed to both practicing my craft and consistently
increasing my skill at my craft. For machine learning researchers, computer
scientists, and engineers, a healthy ongoing dose of theory and practice is a
great way to proceed.

