Name: Brookhaven AIMS Tutorial: K-nearest Neighbors Regression and Applications to Condensed Matter Theory
Start: 2023-01-24T12:00:00-05:00
End: 2023-01-24T13:00:00-05:00
Location: No location set

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

Tuesday, 24 January 2023 - 12:00

Monday, 23 January 2023
Tuesday, 24 January 2023
12:00 K-nearest Neighbors Regression and Applications to Condensed Matter Theory - Jackson Lee (Rutgers University)
K-nearest Neighbors Regression and Applications to Condensed Matter Theory
- Jackson Lee (Rutgers University)
12:00 - 13:00
**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