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

[Sam Tepper]

Hello NBers,

*T**omorrow* at *6:30*, you are invited to join us 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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