[Noisebridge-discuss] Machine Learning seminars?

Jeremy K trochee at gmail.com
Wed Dec 10 21:47:59 UTC 2008


while we're at it, Hastie, Tibshirani & Friedman ("The elements of
statistical learning") is awesome but makes DHS look easy.  I'd show
up and kibitz at at least a few of these if they happen (but no
guarantees that I won't heckle!).

--jeremy

On Wed, Dec 10, 2008 at 1:44 PM, Meredith L. Patterson
<mlp at thesmartpolitenerd.com> wrote:
> Josh Myer wrote:
>> 3.) Data Mining: Practical Machine Learning Tools and Techniques by
>>      Witten and Frank
>>
>> http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0120884070/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1228940709&sr=8-1
>>
>> I rather like this book, but it's a little heavy for introductions.
>
> Witten and Frank has the added bonus that it works hand in hand with
> Weka, which is a solid, mature toolkit that can be used standalone or as
> Java libraries. A++ would classify again.
>
>> I'm also open to suggestions from others; my background is mostly
>> second-hand from practitioners.
>
> There's one other book that I used as an intro when I started working
> with machine learning, but I can't remember the name off the top of my
> head. I'll check my shelves when I get home.
>
> Really, though, once you get past Witten and Frank, you have to leave
> the buffet and pick an area to focus on (Bayesian analysis, perceptrons,
> support vector machines, decision trees, text classification, choose
> your poison).
>
> _Pattern Classification_ by Duda, Hart and Stork is fantastically
> thorough, but heavy on the math; I'd say only about a quarter of it is
> accessible to someone who isn't very, very comfortable with linear
> algebra. (It's possible to view a lot of what the book covers in
> geometric terms instead, but the book doesn't provide a lot of material
> to make that easy.)
>
> --mlp
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