The Google ads auction is a special kind of auction, one you might not know as well as the famous English auction (which we talked about in the last episode). But if it's what Google uses to sell billions of dollars of ad space in real time, you know it must be pretty cool.

Relevant links:

https://en.wikipedia.org/wiki/English_auction

http://people.ischool.berkeley.edu/~hal/Papers/2006/position.pdf

http://www.benedelman.org/publications/gsp-060801.pdf

The Google AdWords algorithm is (famously) an auction system for allocating a massive amount of online ad space in real time--with that fascinating use case in mind, this episode is part one in a two-part series all about auctions. We dive into the theory of auctions, and what makes a "good" auction.

Relevant links:

https://en.wikipedia.org/wiki/English_auction

http://people.ischool.berkeley.edu/~hal/Papers/2006/position.pdf

http://www.benedelman.org/publications/gsp-060801.pdf

A data visualization extravaganza in this episode, as we discuss Chernoff faces (you: "faces? huh?" us: "oh just you wait") and the greatest data visualization of all time, or at least the Napoleonic era.

Relevant links:

http://lya.fciencias.unam.mx/rfuentes/faces-chernoff.pdf

https://en.wikipedia.org/wiki/Charles_Joseph_Minard

Ever tried to visualize a cluster of data points in 40 dimensions? Or even 4, for that matter? We prefer to stick to 2, or maybe 3 if we're feeling well-caffeinated. The t-SNE algorithm is one of the best tools on the market for doing dimensionality reduction when you have clustering in mind.

Relevant links:

https://www.youtube.com/watch?v=RJVL80Gg3lA

The town of [expletive deleted], England, is responsible for the clbuttic [expletive deleted] problem. This week on Linear Digressions: we try really hard not to swear too much.

Related links:

https://en.wikipedia.org/wiki/Scunthorpe_problem

https://www.washingtonpost.com/news/worldviews/wp/2016/01/05/where-is-russia-actually-mordor-in-the-world-of-google-translate/

In order to do supervised learning, you need a labeled training dataset. Or do you...?

Relevant links:

http://www.cs.columbia.edu/~dplewis/candidacy/goldman00enhancing.pdf

Machine learning: it can be fooled, just like you or me. Here's one of our favorite examples, a study into hacking neural networks.

Relevant links:

http://arxiv.org/pdf/1412.1897v4.pdf

Zipf's law is related to the statistics of how word usage is distributed. As it turns out, this is also strikingly reminiscent of how income is distributed, and populations of cities, and bug reports in software, as well as tons of other phenomena that we all interact with every day.

Relevant links:

http://economix.blogs.nytimes.com/2010/04/20/a-tale-of-many-cities/

http://arxiv.org/pdf/cond-mat/0412004.pdf

https://terrytao.wordpress.com/2009/07/03/benfords-law-zipfs-law-and-the-pareto-distribution/

We've gone indie! Which shouldn't change anything about the podcast that you know and love, but we're super excited to keep bringing you Linear Digressions as a fully independent podcast.

Some links mentioned in the show:

https://twitter.com/lindigressions

https://twitter.com/benjaffe

https://twitter.com/multiarmbandit

https://soundcloud.com/linear-digressions

http://lineardigressions.com/

It's Da Vinci meets Skynet: what makes a portrait beautiful, according to a machine learning algorithm. Snap a selfie and give us a listen.