12 Feb
2014
12 Feb
'14
4:55 p.m.
Hello Sudoers,
*T**omorrow* at *6:30*, you are invited to join us, at the fabled
'Noisebridge', as we investigate how to make better inferences using
Bayesian heuristics and algorithms.
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.
If you have your own project, you are encouraged to talk about it and/or
work on it as well.
part of our machine learning
<https://noisebridge.net/wiki/Machine_Learning> series.
Food, beer, and cheer are all greatly appreciated.
Sam
On 02/02/2014 08:48 PM, Sam Tepper wrote:
-------- Original Message --------
Subject: ml class feb 13 (Thurs): Bayesian Inference for everyone:
The (Best) Probability of Causes
Date: Sun, 02 Feb 2014 20:36:16 -0800
From: Sam Tepper <sam.tepper(a)gmail.com>
To: ml(a)lists.noisebridge.net
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.
Bayes rule "cracked the Enigma code, hunted down Russian submarines,
and emerged...from two centuries of controversy"!
Bayesian inference is about how to use Bayes rule (and its
generalizations) to make decisions and conduct optimal rational inquiry.
We'll be looking at data sets of your choice. Add you info here
<https://noisebridge.net/index.php?title=Machine_Learning&action=edit§ion=8>
or contact Mike <cubicgoats(a)gmail.com> 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.
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).
Class starts at 6:30 (7 sharp). Please bring food, beer, and/or good
cheer.
-Sam