Brookhaven AIMS Tutorial: K-nearest Neighbors Regression and Applications to Condensed Matter Theory

Tuesday 24 Jan 2023, 12:00 → 13:00 US/Eastern

In 2023, we have expanded the Brookhaven AIMS series to include also hands-on tutorials organized by the BNL Computational Science Initiative, which are aimed at researchers, educators and students new to machine learning and artificial intelligence. The tutorial presentations are designed for a general audience, and minimal prior experience will be required.

 

1 K-nearest Neighbors Regression and Applications to Condensed Matter Theory

Abstract: In this talk, we’ll be going over the k-nearest neighbors (KNN) regression algorithm, a supervised machine learning algorithm that is used to predict a continuous target value. While KNN is a simple algorithm, it can be used with great effectiveness in many cases. We’ll first develop an intuition behind what the algorithm is doing, and why it works. Then, we’ll walk through a fully coded example problem. After that, we'll apply KNN in a condensed matter context. We’ll end by going over some of the limitations of the algorithm, and how those shortcomings can be addressed.

GitHub link: https://github.com/JackieLee23/KNN-Tutorial

Speaker: Jackson Lee (Rutgers University)

Recording.mp4

Tutorial Notebooks