Learning About Machine Learning 2nd Ed. | Hacker News Reader

Learning About Machine Learning 2nd Ed.

measuringmeasures.com

85 pointsliebke18 comments

For good lectures and slides, see Andrew Moore's collection on statistical data mining:

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

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.

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).

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

Got it. Thanks very much.

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

Seconding his recommendation of Probability Theory: The Logic of Science

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

Baye Sean, relative of Jay Sean? :)

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

Nice catch.

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.

"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.

can you explain why not?

"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.

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

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.

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

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.

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