[ml] intro machine learning meetup: Thursday 12/19 @ 7:00pm

[Mike Schachter]

Hey Sam, have a great holidaycation! I'll try to find someone to transcribe
notes on the wiki for the class.

Noisebot 2.0 seems like a really fun project! We could definitely implement
our own bot that does natural language processing and all that good stuff
too. Using python and all the ML libraries available for it might actually
simplify things...

Maybe we could have a Noisebot 2.0 meetup some time in January?

 mike



On Wed, Dec 18, 2013 at 4:07 PM, Sam Tepper <sam.tepper at gmail.com> wrote:

>  Sadly, I will be unable to attend, as I'm away for the holidays.
> Hopefully someone will be able to post notes to fill in myself and others
> like me.
>
> I wanted to propose a mini-project to investigate, improve, and create.  I
> recently had my attention drawn to the awesome power that is noisebot<https://noisebridge.net/wiki/Noisebot>.
> Apprently it's a stock rbot <http://ruby-rbot.org/rbot-trac/wiki>implimentation that noisebridge has on its irc channel, which uses hidden
> markov models and a whole bunch of other interesting modules.
>
> Unfortunately, it's written in Ruby, and I'm not sure how easy it would be
> to access and change directly.  So I'd recommend people to check it out,
> see what it can do and what it can do better, and think about whether you
> might be interested in working on something like it, either by improving
> the code (Ruby) or creating a new and improved Python/Sage Noisebot 2.0
>
> Anyway, that's just an idea, but I thought it might be nice to check out
> what Noisebridge machine learning implementations we are already using,
> especially one as multi-faceted and public-facing as this.
>
> Cheers,
> Sam
>
>
> On 12/12/2013 06:50 PM, Mike Schachter wrote:
>
>  Next Thursday, in the Church classroom, I will be teaching an
> introductory class in machine learning. Our goal will be straightforward:
> given a dataset, what are the first things to be done? We will go over:
>
>  1) How to read and represent data in python
> 2) How to create and plot features from the data
>  3) How to use histograms to look at the distribution of features
>  4) What is regression? (If there is time)
>
>  No software prerequisites will be necessary, because we will be using
> SAGE (https://cloud.sagemath.com/). All you need is a web browser, your
> brain, and it's support system.
>
>  I am synthesizing a dataset from two sources:
>
> The SIPRI database of international arms transfers:
> http://portal.sipri.org/publications/pages/transfer/splash
>
>  The PRIO database of international armed conflicts:
> http://www.prio.no/Data/Armed-Conflict/
>
>  With such a rich dataset, we can start to examine fun things like the
> relationship between international arms transfers and worldwide armed
> conflicts.
>
>   mike
>
>
>
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