Machine Learning LinkLog #1

This is the first in an occasional series of LinkLog posts. I list links to interesting articles, video and audio items on Machine Learning and allied topics. I group them into three broad categories:

• Introductory Non Technical - Aimed at the general reader or, perhaps, technical manager who wants to learn about Machine Learning, but is not aiming to be a practioner
• Introductory Technical - Aimed at someone who is comfortable with programming and technology, but wishes to learn how to work with Machine Learning tools or techniques
• In Depth Technical - Aimed at someone who is comfortable with the fundamentals of Machine Learning technology, but wants to learn more about a particular aspect or wants to master "day two" problems.

I also intend to indicate whether the item is (mainly) a slidedeck, a video, a single article or a series of items. My goal is to avoid the trope of using pictures of robots to illustrate machine learning topics. However, I apologize in advance if I fail (and you'll certainly find plenty of robot pictures in the items I link to).

Introductory Non Technical

What is Machine Learning?

Short, two-and-a-half minute explainer video from Oxford Sparks ("the amazing stories of science taking place at the University of Oxford"). Accurate and accessible though potentially misleading as it implies that people aren't needed at all. (Note that the doesn't say this directly but you have to listen carefully to pick up that nuance). Avoids the use of unnecessary jargon.
https://www.youtube.com/watch?v=f_uwKZIAeM0

Hype vs. Reality: The AI Explainer

Twenty eight slide explainer deck from Luminary Labs. Covers the different aspects of AI that are currently en vogue. Attempts to predict what is likely to actually succeed and what it probably just hype or misunderstanding.

http://www.slideshare.net/LuminaryLabs/hype-vs-reality-the-ai-explainer

Top 10 Hot Artificial Intelligence (AI) Technologies

An overview of the findings from a Forrester Research report about Artificial Intelligence in 2017.

http://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/#2510ffab42de

Kristen Stewart co-wrote a paper on machine learning

The actress and director co-authored a paper on 'style transfers'. This is a neural-network technique to blend the content of one photo with the style of another. (It is popular with apps such as Prisma). Stewart and her team used the technique in her directoral debut "Come Swim" to create dream-like sequences in the film. The paper describes tricks she used to better control the style transfer effects. This article links in depth technical topics (neural networks and visual processing) with consumer products (feature films).
https://www.engadget.com/2017/01/20/kristen-stewart-paper-style-transfers-come-swim/

Introductory Technical

Machine Learning is Fun

A series of articles introducing various technical aspects of Machine Learning. Available in several languages.https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.nzyqhondf

A Flask API for serving scikit-learn models

Assumes a lot of scikit knowledge, but a good overview of how to create an API using Flask on the front end and scikit on the backend. The API is not truly RESTful (the endpoints are verbs not nouns, for example). However, it is still a useful introduction and contains the full Python code.
https://medium.com/@amirziai/a-flask-api-for-serving-scikit-learn-models-c8bcdaa41daa#.ti049hnb2

Hitchhiker's Guide to Data Science, Machine Learning, R, Python

A "best of" collection of links, relating to Machine Learning and Data Science, with a particular emphasis on Python and R.

http://www.datasciencecentral.com/profiles/blogs/hitchhiker-s-guide-to-data-science-machine-learning-r-python

In Depth Technical

Google's 43 Practical Rules of Machine Learning in Industry

Martin Zinkevich compiled 43 rules "intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google." Here are three(!) different presentations of those rules.

• The original PDF http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
• A Github set of pages, for those who prefer linkable text https://github.com/thundergolfer/google-rules-of-machine-learning
• A zine versions via http://jvns.ca/zines/ for those who prefer comics http://jvns.ca/production-machine-learning.pdf
• The zine version in print-n-fold form http://jvns.ca/production-machine-learning-print.pdf

Predicting with confidence: the best machine learning idea you never heard of

An article advocating the use of "conformal prediction" a technique for calculating conformance intervals for your machine learning predictions, no matter what the forecast technique or dataset you're working with.

https://scottlocklin.wordpress.com/2016/12/05/predicting-with-confidence-the-best-machine-learning-idea-you-never-heard-of/