Linear Regressions

Computers are capable of many impressive feats, but making you laugh is usually not one of them. Or could it be? This episode will talk about a custom-built machine learning algorithm that searches through text and writes jokes based on what it finds. 

The jokes are formulaic: they're all of the form "I like my X like I like my Y: Z" where X and Y are nouns, and Z is an adjective that can describe both X and Y. For (dumb) example, "I like my men like I like my coffee: steaming hot." The joke is funny when ZX and ZY are both very common phrases, but X and Y are rarely seen together. 

So, given a large enough corpus of text, the algorithm looks for triplets of words that fit this description and writes jokes based on them. Are the jokes funny? You be the judge...

http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervised.pdf

Computers Try to Tell Jokes

A true classic topic in ML: Facial recognition is very high-dimensional, meaning that each picture can have millions of pixels, each of which can be a single feature. It's computationally expensive to deal with all these features, and invites overfitting problems. PCA (principal components analysis) is a classic dimensionality reduction tool that compresses these many dimensions into the few that contain the most variation in the data, and those principal components are often then fed into a classic ML algorithm like and SVM. 

One of the best thing about eigenfaces is the great example code that you can find in sklearn--you can distinguish pictures of world leaders yourself in just a few minutes!

http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html

Facial Recognition with Eigenfaces

Let’s talk money. As a “hot” career right now, data science can pay pretty well. But for an individual person matched with a specific job or industry, how much should someone expect to make? 

Since Katie was on the job market lately, this was something she’s been researching, and it turns out that data science itself (in particular linear regressions) has some answers. 

In this episode, we go through a survey of hundreds of data scientists, who report on their job duties, industry, skills, education, location, etc. along with their salaries, and then talk about how this data was fed into a linear regression so that you (yes, you!) can use the patterns in the data to know what kind of salary any particular kind of data scientist might expect.

Careers in Data Science

There's a good chance that great data science is going on close to you, and that it's going toward making your city, state, country, and planet a better place.  Not all the data science questions being tackled out there are about finding the sleekest new algorithm or billion-dollar company idea--there's a whole world of social data science that just wants to make the world a better place to live in.

Data Science for Making the World a Better Place

As you may have heard, a computer beat a world-class human player in Go last week.  As recently as a year ago the prediction was that it would take a decade to get to this point, yet here we are, in 2016.  We'll talk about the history and strategy of game-playing computer programs, and what makes Google's AlphaGo so special.

Relevant link:
http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html

Let's imagine a future in which a truly intelligent computer program exists.  How would it convince us (humanity) that it was intelligent?  Alan Turing's answer to this question, proposed over 60 years ago, is that the program could convince a human conversational partner that it, the computer, was in fact a human.  60 years later, the Turing Test endures as a gold standard of artificial intelligence.  It hasn't been beaten, either--yet.

Relevant links:
https://en.wikipedia.org/wiki/Turing_test
http://commonsensereasoning.org/winograd.html
http://consumerist.com/2015/09/29/its-not-just-you-robots-are-also-bad-at-assembling-ikea-furniture/

The Turing Test

Survival analysis is all about studying how long until an event occurs--it's used in marketing to study how long a customer stays with a service, in epidemiology to estimate the duration of survival of a patient with some illness, and in social science to understand how the characteristics of a war inform how long the war goes on.  This episode talks about the special challenges associated with survival analysis, and the tools that (data) scientists use to answer all kinds of duration-related questions.

Survival Analysis

As more and more data gets collected seemingly every day, and data scientists use that data for modeling, the technical limits associated with machine learning on big datasets keep getting pushed back.  This week is a first-hand case study in using scikit-learn (a popular python machine learning library) on multi-terabyte datasets, which is something that Katie does a lot for her day job at Civis Analytics.  There are a lot of considerations for doing something like this--cloud computing, artful use of parallelization, considerations of model complexity, and the computational demands of training vs. prediction, to name just a few.

Linear Regressions

Edward Huh