On 18 Oct 2016, I gave a talk at Austin ACM SIGKDD on the *k*-nearest neighbors
algorithm. Topics included some machine learning theory (approximation vs.
generalization, VC dimension), the algorithm itself, proving the algorithm's
performance, and some practical concerns around choosing *k*.

Some other topics that I would probably include next time are similarity functions, high-dimensional spaces, and categorical data.

You can find the slides here. Note that my presentation probably won't make a ton of sense from these slides, as they were mostly aids to the words I was saying out loud. If you've got any questions, feel free to email me; I'd love to chat!

Thanks to everyone who came to watch; I appreciated hearing your feedback!