[Air-L] Seeking resources on A.I. and machine learning

[Ranjodh Singh Dhaliwal]

Hi everyone,

This is such a wonderful collection of resources here!
I'd like to add to the list Adrian Mackenzie's *Machine Learners*
<https://mitpress.mit.edu/books/machine-learners>. Also of interest may be
Matteo Pasquinelli's work
<http://www.glass-bead.org/article/machines-that-morph-logic/?lang=enview>.
Finally, if anyone wishes to start to get at the technical implementations
or the pedagogical approaches to ML in Computer Sciencey circles, Andrew
Ng's course here <https://www.coursera.org/learn/machine-learning> is the
go-to for a number of people, so the introduction there may be of limited
use.

Cheers/ranjodh

On Fri, 27 Jul 2018 at 09:45 Mcglotten, Shaka <shaka.mcglotten at purchase.edu>
wrote:

> Thanks all for sharing these! Jenny, is it ok to add to this list? I’m
> working on a piece now and have found some articles that would fit in
> nicely.
>
> Also, given the sorts of materials reflected in the materials already
> exchanged, I hope some of you might like to become part of the Center for
> Race and Digital Studies, link below.
>
> Best,
> —
> Shaka McGlotten (they/he)
> Associate Professor, Media Studies
> Coordinator, Gender Studies & Global Black Studies
>
> Center for Critical Race and Digital Studies<
> https://criticalracedigitalstudies.com/>
>
>
>
>
>
>
> On Jul 25, 2018, at 5:20 PM, Tracey P. Lauriault <tlauriau at gmail.com
> <mailto:tlauriau at gmail.com>> wrote:
>
> https://www.fatml.org/
>
> On Tue, Jul 24, 2018 at 5:17 PM, Emma Stamm <stamm at vt.edu> wrote:
>
> Dear AoIRists,
>
> I am researching artificial intelligence and machine learning within the
> framework of digital culture studies and philosophy of technoscience. Right
> now, I am looking for articles that reflect current developments in AI & ML
> programming that are suitable for non-STEM experts. Specifically, I am
> interested in pieces that illuminate the ways in which generalization and
> inductive/abductive reasoning are essential to algorithms that effectively
> “predict” the future. However, more wide-ranging, introductory pieces would
> also be helpful for me.
>
> I am familiar with the work of Pedro Domingos, but do not know of many
> other sources that suit my needs. Any recommendations would be very
> welcome.
>
> Thank you in advance!
>
> Best,
>
> Emma
>
> --
> *Emma Stamm*
> *PhD Student, ASPECT Virginia Tech
> <https://liberalarts.vt.edu/departments-and-schools/
> alliance-for-social-political-ethical-and-cultural-thought.html>*
> *o-culus.com <http://o-culus.com> | @turing_tests*
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>
>
> --
> *Tracey P. Lauriault*
>
> Assistant Professor
> Critical Media Studies and Big Data
> Communication Studies
> School of Journalism and Communication
> Suite 4110, River Building
> Carleton University
> 1125 Colonel By Drive
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>
> 1-613-520-2600 x7443 <(613)%20520-2600>
> Tracey.Lauriault at Carleton.ca
> @TraceyLauriault
> Skype: Tracey.P.Lauriault
> https://carleton.ca/sjc/people-archives/lauriault-tracey/
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-- 

Ranjodh Singh Dhaliwal

https://www.ranjodhdhaliwal.com/

PhD Candidate in English with an emphasis in Science and Technology
Studies, UC Davis.


Sent from a cellphone. Please excuse any typos.