http://pad.constantvzw.org/public_pad/TouchingCorrelationsWorkshop
Courtenay Cotton - cvcotton@gmail.com - Please email me with followup questions!
Touching Correlations Program
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Day 1:
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*10.00 - 10.30
*Welcome!
*10.30 - 12.00
*Introductions
*
*12:00 - 13.30
*Courtenay leads introduction to Machine Learning
*
*
*13.30 - 14.30
*LUNCH
**
*14.30 - 16.00
*WEKA session 1
*explore interface, dataset, first classifiers
*
*
*HOMEWORK for Day 1: Find a dataset of your liking (bring your own dataset)
*see for examples here:
*http://archive.ics.uci.edu/ml/
*https://www.kaggle.com/competitions
*http://www.kdnuggets.com/datasets/index.html
Day 2:
10.00 - 11.30
*Courtenay leads discussion on correlations, ML vs statistics, validation
11.30 - 13.00
*WEKA session 2
*classification (and maybe clustering)
13.00 - 14.00
*LUNCH
14.00 - 15.00
Videos:
sentiment analysis: https://www.youtube.com/watch?v=ytUHvMNnzZk
advertising application: https://www.youtube.com/watch?v=EQhwNcQhP4g
*WEKA Session 3
*working with your own datasets
15.00 - 16.00
*Discussion and steps forward
*
*
Neural Networks - Deep dream:
http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html
http://motherboard.vice.com/read/why-googles-neural-networks-look-like-theyre-on-acid
http://rhizome.org/editorial/2015/jul/10/deep-dream-doggy-monster/
FURTHER REFERENCES (please add!):
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Workshop Slides:
https://docs.google.com/presentation/d/1xrpLDvXkyzsYLFSD1ZQO6aNndw5DPC9ZnJ2CGnr9aKo/edit?usp=sharing
Writings On Machine Learning Algorithms:
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*Louder vowels won’t get you laid, and other tales of spurious correlation
*http://arstechnica.com/science/2015/06/louder-vowels-wont-get-you-laid-and-other-tales-of-spurious-correlation-2/
*Politique des Algorithmes (in French and in English)
*http://www.scoop.it/t/politique-des-algorithmes
Deep Learning:
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Google's Deep Dream Project:
*
*Blog Post by Google: Inceptionism: Going deeper into Neural Networks
*http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html
*Deep Dream Code on Github:
*https://github.com/google/deepdream
*http://googleresearch.blogspot.com/2015/07/deepdream-code-example-for-visualizing.html
*Yes, androids do dream of electric sheep
*http://www.theguardian.com/technology/2015/jun/18/google-image-recognition-neural-network-androids-dream-electric-sheep
*And how it might be used:
*https://gigaom.com/2013/09/13/why-machine-learning-might-be-a-wearable-cameras-best-friend/
*
*Google and Spam:
*http://www.csmonitor.com/Technology/2015/0713/Google-fights-spam-with-artificial-intelligence
*
Algorithms of the Mind: What machine learning teaches us about ourselves?
https://medium.com/deep-learning-101/algorithms-of-the-mind-10eb13f61fc4
Computer Science Paper: Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
*http://arxiv.org/abs/1412.1897
*
*The unreasonable effectiveness of Recurrent Neural Networks
*https://karpathy.github.io/2015/05/21/rnn-effectiveness/
Fiction:
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Speak - A novel by Louisa Hall on artificial intelligence:
http://www.harpercollins.com/9780062391193/speak/web-sampler
Methodology:
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Sedgwick has a book *Touching Feeling* (Duke UP)
- eve sedgwick: paranoid reading of secret structures of power that govern our lives. reading with negative affect. telling conspiracy stories in a paranoid way.
- latour thinks the paranoid conspiracy theory is itself a power move
- sedgwick looks to queer theory, queer communities to show that vulnerability leads to paranoia. you need stories big enough to combat feeling vulnerable. suggests reparative reading many ways communities extract sustenance from the objects of a culture whose avowed desire has been not to sustain them
- Paranoid reading vs reparative reading: https://girlpower1.wordpress.com/2013/12/02/paranoid-vs-reparative-reading/
- The chapter "You're so paranoid, you probably think this introduction is about you" https://nonoedipal.files.wordpress.com/2009/09/paranoid-reading-and-reparative-reading.pdf
Weka tutorials:
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http://machinelearningmastery.com/how-to-run-your-first-classifier-in-weka/
http://www.ibm.com/developerworks/opensource/library/os-weka1/index.html
http://www.ibm.com/developerworks/opensource/library/os-weka2/index.html
http://www.ibm.com/developerworks/opensource/library/os-weka3/index.html
Weka Course and associated Videos:
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https://weka.waikato.ac.nz/dataminingwithweka/preview
https://www.youtube.com/watch?v=Exe4Dc8FmiM
Machine Learning Ontology:
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http://www.datascienceontology.com/
Tutorials and videos for further learning:
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http://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
http://machinelearningmastery.com/how-to-build-an-intuition-for-machine-learning-algorithms/
http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning
https://wakelet.com/wake/atfflSHf5/data-science
Statistics vs. Machine Learning:
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*The two cultures: statistics vs. machine learning
*http://stats.stackexchange.com/questions/6/the-two-cultures-statistics-vs-machine-learning
*http://econometricsense.blogspot.com/2011/01/classical-statistics-vs-machine.html
*
*Ramsay (2003: 173) notes, when one is not looking for a ‘right’ classification but for an ‘interesting’ pattern, many of the traditional statistical measures of evaluation are of little use: ‘Empirical validation and hypothesis testing simply make no sense in a discourse where the object is not to be right (in the sense that a biologist is ever “right”), but to be interesting (in the sense that a great philosopher is “interesting”'.
On CQRRELATIONS: the project that inspired us!
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*Cqrrelations is a project of Constant VZW
* http://www.cqrrelations.constantvzw.org/1x0/
*for those of you who would like to work with them, they organize a yearly summer school called re-learn:
* http://relearn.be/2015/
References and works cited
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- Lucy Suchman, Human Machine-Reconfigurations (Cambridge U Press, 2007)
- Culturally Embedded Computing at Cornell (led by Phoebe Sengers: http://infosci.cornell.edu/faculty/phoebe-sengers)
- Values in Design workshops:
- Dat project (http://dat-data.com)
Symposium on Obfuscation:
http://obfuscationsymposium.org
Rachel Law's Vortex is listed here:
http://obfuscationsymposium.org/obfuscation-tools/
I am taking notes here: http://pad.constantvzw.org/public_pad/notesTouchingCorrelationsWorkshopSeda
If you want, come join! :)
Anne Fausto Sterling "Bare Bones of Sex Part 1" 2005 -
shh my link - https://www.dropbox.com/s/s4wmggbrwm43f8h/Fausto-Sterling%20-%20Unknown%20-%20The%20Bare%20Bones%20of%20Gender.pdf?dl=0
paywall official link - http://www.jstor.org/stable/10.1086/424932
How do we tell stories about big data? What major governmental-industry projects are legitimized by big data methods? -- absolutely, why does harvesting and processing data accumulate power to decide and act and how?
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Chelsea Clinton: Internet Access Is Key to Gender Equality
- http://www.wired.com/2015/03/chelsea-clinton-no-ceilings/
What governmental modes of operating and knowing come out of big data practices? what social dilemmas, issues around the distribution of power do they seem to solve? (governmentality, power/knowledge, biopolitics)
Heidi Schelhowe who was a big figure in feminist critique of computing in Germany in the 90s and 00s:
http://dimeb.informatik.uni-bremen.de/documents/artikel.2005.Schelhowe.Paradigms.pdf
who was also instrumental in getting informatica feminale, a summer school for women in computing off the ground:
https://www.informatica-feminale.de
Data sets we found:
http://www.thenewyorkworld.com/ as an alternative https://nycopendata.socrata.com/
Wikileaks CSV
Piketty's data on inequality
Can someone post the link to the PDF of the Weka book? I didn't get it. - lilly
Weka data mining book: http://www.cse.hcmut.edu.vn/~chauvtn/data_mining/Texts/[7]%20Data%20Mining%20-%20Practical%20Machine%20Learning%20Tools%20and%20Techniques%20(3rd%20Ed).pdf