Linear Digressions

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Technology
**Linear Digressions**

Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago. 896520

Latest Episodes:

Fractional Dimensions May 29, 2017

Show notes

We chat about fractional dimensions, and what the actual heck those are.

Things You Learn When Building Models for Big Data May 22, 2017

Show notes

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.

How to Find New Things to Learn May 15, 2017

Show notes

If you're anything like us, you a) always are curious to learn more about data science and machine learning and stuff, and b) are usually overwhelmed by how much content is out there (not all of it very digestible). We hope this podcast is a part of the solution for you, but if you're looking to go farther (who isn't?) then we have a few new resources that are presenting high-quality content in a fresh, accessible way. Boring old PDFs full of inscrutable math notation, your days are numbered!

Federated Learning May 08, 2017

Show notes

As machine learning makes its way into more and more mobile devices, an interesting question presents itself: how can we have an algorithm learn from training data that's being supplied as users interact with the algorithm? In other words, how do we do machine learning when the training dataset is distributed across many devices, imbalanced, and the usage associated with any one user needs to be obscured somewhat to protect the privacy of that user? Enter Federated Learning, a set of related algorithms from Google that are designed to help out in exactly this scenario. If you've used keyboard shortcuts or autocomplete on an Android phone, chances are you've encountered Federated Learning even if you didn't know it.

Word2Vec May 01, 2017

Show notes

Word2Vec is probably the go-to algorithm for vectorizing text data these days. Which makes sense, because it is wicked cool. Word2Vec has it all: neural networks, skip-grams and bag-of-words implementations, a multiclass classifier that gets swapped out for a binary classifier, made-up dummy words, and a model that isn't actually used to predict anything (usually). And all that's before we get to the part about how Word2Vec allows you to do algebra with text. Seriously, this stuff is cool.

Feature Processing for Text Analytics Apr 24, 2017

Show notes

It seems like every day there's more and more machine learning problems that involve learning on text data, but text itself makes for fairly lousy inputs to machine learning algorithms. That's why there are text vectorization algorithms, which re-format text data so it's ready for using for machine learning. In this episode, we'll go over some of the most common and useful ways to preprocess text data for machine learning.

Education Analytics Apr 17, 2017

Show notes

This week we'll hop into the rapidly developing industry around predictive analytics for education. For many of the students who eventually drop out, data science is showing that there might be early warning signs that the student is in trouble--we'll talk about what some of those signs are, and then dig into the meatier questions around discrimination, who owns a student's data, and correlation vs. causation. Spoiler: we have more questions than we have answers on this one. Bonus appearance from Maeby the dog, who isn't a data scientist but does like to steal food off the counter.

A Technical Deep Dive on Stanley, the First Self-Driving Car Apr 10, 2017

Show notes

In our follow-up episode to last week's introduction to the first self-driving car, we will be doing a technical deep dive this week and talking about the most important systems for getting a car to drive itself 140 miles across the desert. Lidar? You betcha! Drive-by-wire? Of course! Probabilistic terrain reconstruction? Absolutely! All this and more this week on Linear Digressions.

An Introduction to Stanley, the First Self-Driving Car Apr 03, 2017

Show notes

In October 2005, 23 cars lined up in the desert for a 140 mile race. Not one of those cars had a driver. This was the DARPA grand challenge to see if anyone could build an autonomous vehicle capable of navigating a desert route (and if so, whose car could do it the fastest); the winning car, Stanley, now sits in the Smithsonian Museum in Washington DC as arguably the world's first real self-driving car. In this episode (part one of a two-parter), we'll revisit the DARPA grand challenge from 2005 and the rules and constraints of what it took for Stanley to win the competition. Next week, we'll do a deep dive into Stanley's control systems and overall operation and what the key systems were that allowed Stanley to win the race.

Feature Importance Mar 27, 2017

Show notes

Figuring out what features actually matter in a model is harder to figure out than you might first guess. When a human makes a decision, you can just ask them--why did you do that? But with machine learning models, not so much. That's why we wanted to talk a bit about both regularization (again) and also other ways that you can figure out which models have the biggest impact on the predictions of your model.

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