I am very much wanting the more doing / project as-a-group thing. <br><br>I probably won't make it to the ML meeting this coming week tho (most 3/4 weeks I am there)<br><br>There are two perspectives of what Machine Learning is, might call for lack of better terms the Stanford perspective and the Carnegie-Mellon perspective (from the location of the authors of two popular texts), or East and West if you like.<br>
<br>I generally take the East side and Mike the west, but also would like to work on more general problems with a group, maybe less Statistical Learning ... and / or problems which have orderings rather than actual or precise numerical values (ordinal variables), so that optimization is possible even if formulas cannot be given or numerical data is either too imprecise or simply unavailable. [Like if you have labels like letter grades, but not numerically based, how best to assign / design content ?]<br>
<br>Maybe applies to some problems in knowledge management, natural language / linguistics problems, policy decision making (how to maximize job satisfaction for instance), intelligence analysis.<br><br>Kinda short on specific problems, might have to Troll Search them. <br>
<br>By the way, what's the best way to produce a table of contents rather than an index (given say a bunch of text scraped off the Web) ? <br><br><div class="gmail_quote">On Fri, May 27, 2011 at 1:27 PM, Crutcher Dunnavant <span dir="ltr"><<a href="mailto:crutcher@gmail.com">crutcher@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">The ML group seems to have grown up quite a bit since the last time I paid attention; and I think I should start participating; as the page lists many things I'd like to learn and play with.<div>
<br></div><div>I am specifically suggesting a group-project oriented group; rather than a research group or class. Something that would yield finished projects; something where we collaborate on a common code base and problem set, beat it to death, publish it (5mof?); and move on to the next one.</div>
<div><br></div><div>There are many applications of metaheuristic search outside machine learning; and I don't want to hijack a group which looks healthy.</div><div><div><div></div><div class="h5"><br><div class="gmail_quote">
On Fri, May 27, 2011 at 12:00 PM, Mike Schachter <span dir="ltr"><<a href="mailto:mike@mindmech.com" target="_blank">mike@mindmech.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Crutcher,<br>
<br>
I'd be interested in black box optimization. The machine learning<br>
group meets up on Wednesdays at 7:30pm in the Church classroom:<br>
<br>
<a href="https://www.noisebridge.net/index.php?title=Machine_Learning" target="_blank">https://www.noisebridge.net/index.php?title=Machine_Learning</a><br>
<br>
Just speaking for myself, I'd be happy to see you share time/space<br>
with the ML group to talk about optimization, as it's a core part of<br>
machine learning.<br>
<br>
We don't have anything going on next week, and you're welcome to<br>
come in to talk about stuff, I'd be happy to discuss optimization with<br>
you!<br>
<br>
mike<br>
<div><div></div><div><br>
<br>
<br>
On Fri, May 27, 2011 at 10:10 AM, Crutcher Dunnavant <<a href="mailto:crutcher@gmail.com" target="_blank">crutcher@gmail.com</a>> wrote:<br>
> I am very interested in starting a black box optimization search group at<br>
> noisebridge. This field is called "metaheauristics"; but the name is a<br>
> stupid historical artifact (so says everyone in the field).<br>
> Optimization is, given a function f(x), searching for the x which yields the<br>
> best f(x). Black box optimization is a sub-field of optimization where you<br>
> can't analyize the function f to determine what values of x are likely to be<br>
> good; so you have to search the space for them.<br>
> The following algorithms are ALL metaheuristic optimization:<br>
> Hill Climbing (aka. Gradient Assent/Descent)<br>
> Genetic Search<br>
> Genetic Programming<br>
> Ant Colony Systems<br>
> Particle Swarm Optimization<br>
> I've recently read a fabulous undergraduate text on the subject, very<br>
> approachable, called "Essentials of Metaheuristics".<br>
> The book in question is available from Lulu and Amazaon:<br>
> <a href="http://www.cs.gmu.edu/%7Esean/book/metaheuristics/" target="_blank">http://www.cs.gmu.edu/~sean/book/metaheuristics/</a><br>
> or you can just download the PDF.<br>
> <a href="http://www.cs.gmu.edu/%7Esean/book/metaheuristics/Essentials.pdf" target="_blank">http://www.cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf</a><br>
><br>
> If you aren't sure what I'm talking about, read the first chapter or two. If<br>
> you have a background in programming, you should be able to follow it<br>
> trivially.<br>
> What I want TrollSearch to do: Build Shit<br>
> Let's find interesting problems; and build search algorithms over them. This<br>
> can apply to evolving good fit 3d models for the printer; making techno; or<br>
> identifying penii.<br>
> I'd like TrollSearch to look much more like SpaceBridge than like the Python<br>
> Class.<br>
> Please comment in-thread if you are interested.<br>
> --<br>
> Crutcher Dunnavant <<a href="mailto:crutcher@gmail.com" target="_blank">crutcher@gmail.com</a>><br>
><br>
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</blockquote></div><br><br clear="all"><br></div></div>-- <br><div class="im">Crutcher Dunnavant <<a href="mailto:crutcher@gmail.com" target="_blank">crutcher@gmail.com</a>><br>
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