Resources on Machine Learning
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Class: Topics in Social Media


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

Feature Selection to Improve Accuracy and Decrease Training Time
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http://machinelearningmastery.com/feature-selection-to-improve-accuracy-and-decrease-training-time/


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

Videos:    
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sentiment analysis: https://www.youtube.com/watch?v=ytUHvMNnzZk
advertising application: https://www.youtube.com/watch?v=EQhwNcQhP4g
Reinforcement Learning: https://www.youtube.com/watch?v=xM62SpKAZHU

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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/

Google Tensorflow (Machine learning engine that is open source)
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https://hacked.com/meet-googles-new-open-source-machine-learning-tool-tensorflow/

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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• Goofy Deep Learning Video:
• https://www.youtube.com/watch?v=bHvf7Tagt18

    Google's Deep Dream Project:

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  • 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/
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• Google and Spam:
• http://www.csmonitor.com/Technology/2015/0713/Google-fights-spam-with-artificial-intelligence
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     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
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• The unreasonable effectiveness of Recurrent Neural Networks
• https://karpathy.github.io/2015/05/21/rnn-effectiveness/

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
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• 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”)'.
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Machine Learning vs. Artificial Intelligence:
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https://www.youtube.com/watch?v=WXHM_i-fgGo

Fiction:
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    Speak - A novel by Louisa Hall on artificial intelligence:
http://www.harpercollins.com/9780062391193/speak/web-sampler


absolute geekiness on overfitting:
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https://www.youtube.com/watch?v=fJMXDlNkYvU

Torch7
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Torch7 is an easy to use MATLAB-like environment for machine learning applications. It uses lua, a
simple scripting language, and underlying C with GPUs (CUDA) for fast, parallel processing.
Code & README: https://github.com/torch/torch7
NYU CILVR Lab's Page: http://cilvr.nyu.edu/doku.php?id=software:torch:start
Tutorial: http://code.madbits.com/wiki/doku.php?id=tutorial

Microsoft's Distributed Machine Learning Toolkit
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http://www.dmtk.io/

Fun Examples
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How the delivery service Postmates estimates delivery times: http://engineering.postmates.com/Estimating-Delivery-Times/
Analyzing live movement: http://blog.telenor.io/2015/10/26/machine-learning.html
Rap lyrics generator using maching learning: http://www.deepbeat.org/
Identifying author of an email: http://blog.brainattica.com/machine-learning-for-indentify-the-author-of-an-email/
Google Smart Reply uses neural networks to write probable email responses for you: http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html