No “That” Left Behind? I came across a post on Language Log last week giving some evidence that Obama tends to add that to the prepared version of his speeches. For example, in a recent speech at George Washington University, … Continue reading → [Read more...]

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How do you know what machine learning algorithm to choose for your classification problem? Of course, if you really care about accuracy, your best bet is to test out a couple different ones (making sure to try different parameters within each algorithm as well), and select the best one by ... [Read more...]

Kickstarter is an online crowdfunding platform for launching creative projects. When starting a new project, project owners specify a deadline and the minimum amount of money they need to raise. They receive the money (less a transaction fee) only if … Continue reading → [Read more...]

(For a layman’s introduction, see here.) Least Angle Regression (aka LARS) is a model selection method for linear regression (when you’re worried about overfitting or want your model to be easily interpretable). To motivate it, let’s consider some other model selection methods: Forward selection starts with no ... [Read more...]

Introduction Suppose you see two drunks (i.e., two random walks) wandering around. The drunks don’t know each other (they’re independent), so there’s no meaningful relationship between their paths. But suppose instead you have a drunk walking with her dog. This … Continue reading → [Read more...]

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I describe a kernel density approach to outlier detection on small datasets. In particular, my model is the set of prices for a given item that can be found online. Introduction Suppose you’re searching online for the cheapest place to … Continue reading → [Read more...]

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The Kahn, Saks, and Sturtevant approach to the Evasiveness Conjecture (see the original paper here) is an epic application of pure mathematics to computer science. I’ll give an overview of the approach here, and probably try to add some more information on the problem in other posts. tl;dr ... [Read more...]

(Way back when, I went through all the Netflix prize papers. I’m now (very slowly) trying to clean up my notes and put them online. Eventually, I hope to have a more integrated tutorial, but here’s a rough draft for now.) This is a summary of Bell and ... [Read more...]

Measure theory studies ways of generalizing the notions of length/area/volume. Even in 2 dimensions, it might not be clear how to measure the area of the following fairly tame shape: much less the “area” of even weirder shapes in higher dimensions or different spaces entirely. For example, suppose you ... [Read more...]

(Way back when, I went through all the Netflix prize papers. I’m now (very slowly) trying to clean up my notes and put them online. Eventually, I hope to have a more integrated tutorial, but here’s a rough draft for now.) This is a summary of Koren’s 2008 ... [Read more...]

Suppose you’re very indecisive, so whenever you want to watch a movie, you ask your friend Willow if she thinks you’ll like it. In order to answer, Willow first needs to figure out what movies you like, so you give her a bunch of movies and tell her ... [Read more...]

Lots of people know that the Riemann Hypothesis has something to do with prime numbers, but most introductions fail to say what or why. I’ll try to give one angle of explanation. Layman’s Terms Suppose you have a bunch of friends, each with an instrument that plays at ... [Read more...]

Introduction In making its product recommendations, Amazon makes heavy use of an item-to-item collaborative filtering approach. This essentially means that for each item X, Amazon builds a neighborhood of related items S(X); whenever you buy/look at an item, Amazon then recommends you items from that item’s neighborhood. ... [Read more...]