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<div class="moz-forward-container"> Hello Sudoers,<br>
<br>
<b>T</b><b>omorrow</b> at <b>6:30</b>, you are invited to join
us, at the fabled 'Noisebridge', as we investigate how to make
better inferences using Bayesian heuristics and algorithms. <br>
<br>
You are welcome to bring your own data, qnd/or you can take part
in designing and implementing your very own Bayesian spam filter
on the infamous noisebridge-discuss archive. <br>
<br>
If you have your own project, you are encouraged to talk about it
and/or work on it as well. <br>
part of our <a moz-do-not-send="true"
href="https://noisebridge.net/wiki/Machine_Learning">machine
learning</a> series. <br>
<br>
Food, beer, and cheer are all greatly appreciated.<br>
<br>
Sam<br>
<br>
<br>
<div class="moz-cite-prefix">On 02/02/2014 08:48 PM, Sam Tepper
wrote:<br>
</div>
<blockquote cite="mid:52EF1F87.1080801@gmail.com" type="cite"> <br>
<div class="moz-forward-container"><br>
<br>
-------- Original Message --------
<table class="moz-email-headers-table" border="0"
cellpadding="0" cellspacing="0">
<tbody>
<tr>
<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Subject:
</th>
<td>ml class feb 13 (Thurs): Bayesian Inference for
everyone: The (Best) Probability of Causes</td>
</tr>
<tr>
<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Date:
</th>
<td>Sun, 02 Feb 2014 20:36:16 -0800</td>
</tr>
<tr>
<th align="RIGHT" nowrap="nowrap" valign="BASELINE">From:
</th>
<td>Sam Tepper <a moz-do-not-send="true"
class="moz-txt-link-rfc2396E"
href="mailto:sam.tepper@gmail.com"><sam.tepper@gmail.com></a></td>
</tr>
<tr>
<th align="RIGHT" nowrap="nowrap" valign="BASELINE">To:
</th>
<td><a moz-do-not-send="true"
class="moz-txt-link-abbreviated"
href="mailto:ml@lists.noisebridge.net">ml@lists.noisebridge.net</a></td>
</tr>
</tbody>
</table>
<br>
<br>
Join us for a night of statistics and machine learning for all
levels of skill and comfort at the fabled hacker heritage site
in SF, Noisebridge.<br>
<br>
Bayes rule "cracked the Enigma code, hunted down Russian
submarines, and emerged...from two centuries of controversy"!<br>
<br>
Bayesian inference is about how to use Bayes rule (and its
generalizations) to make decisions and conduct optimal
rational inquiry.<br>
<br>
We'll be looking at data sets of your choice. Add you info <a
moz-do-not-send="true"
href="https://noisebridge.net/index.php?title=Machine_Learning&action=edit§ion=8">here</a>
or contact <a moz-do-not-send="true"
href="cubicgoats@gmail.com">Mike</a> if you want to add your
data set to our git repository, and discuss/collaborate on
your data for the class. I may also provide data I've
acquired from some interesting external sources.<br>
<br>
If you're using Bayesian statistics or would like to use
Bayesian statistics on your own, we will also talk about best
practices and powerful tools that everyone can use (such as
may be found in some Python libraries and AI algorithms).<br>
<br>
Class starts at 6:30 (7 sharp). Please bring food, beer,
and/or good cheer. <br>
<br>
-Sam<br>
<br>
</div>
<br>
</blockquote>
<br>
<br>
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