Linear Digressions

Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!

Because there are more interesting problems than there are labeled datasets, semi-supervised learning provides a framework for getting feedback from the environment as a proxy for labels of what's "correct." Of all the machine learning methodologies, it might also be the closest to how humans usually learn--we go through the world, getting (noisy) feedback on the choices we make and learn from the outcomes of our actions.

Overfitting to your training data can be avoided by evaluating your machine learning algorithm on a holdout test dataset, but what about overfitting to the test data? Turns out it can be done, easily, and you have to be very careful to avoid it. But an algorithm from the field of privacy research shows promise for keeping your test data safe from accidental overfitting

How many data scientists are there, where do they live, where do they work, what kind of tools do they use, and how do they describe themselves? RJMetrics wanted to know the answers to these questions, so they decided to find out and share their analysis with the world. In this very special interview episode, we welcome Tristan Handy, VP of Marketing at RJMetrics, who will talk about "The State of Data Science Report."

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.

The Kalman Filter is an algorithm for taking noisy measurements of dynamic systems and using them to get a better idea of the underlying dynamics than you could get from a simple extrapolation. If you've ever run a marathon, or been a nuclear missile, you probably know all about these challenges already. By the way, we neglected to mention in the episode: Katie's marathon time was 3:54:27!

When you sleep, the neural pathways in your brain take the "white noise" of your resting brain, mix in your experiences and imagination, and the result is dreams (that is a highly unscientific explanation, but you get the idea). What happens when neural nets are put through the same process? Train a neural net to recognize pictures, and then send through an image of white noise, and it will start to see some weird (but cool!) stuff.

Sometimes numbers are... weird. Benford's Law is a favorite example of this for us--it's a law that governs the distribution of the first digit in certain types of numbers. As it turns out, if you're looking up the length of a river, the population of a country, the price of a stock... not all first digits are created equal.

Not to oversell it, but the student's t-test has got to have the most interesting history of any statistical test. Which is saying a lot, right? Add some boozy statistical trivia to your arsenal in this epsiode.

Doing some science, and want to know if you might have found something? Or maybe you've just accomplished the scientific equivalent of going fishing and reeling in an old boot? Frequentist p-values can help you distinguish between "eh" and "oooh interesting". Also, there's a lot of physics in this episode, nerds.