[sudo-discuss] Tomorrow: Bayesian Inference for Everyone: The (best) Probability of Causes

[Sam Tepper]

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 at gmail.com>
> To: 	ml at 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&section=8>
> or contact Mike <cubicgoats at 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
>
>



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