Dimensionality Reduction

• Matthew Carbone (Brookhaven National Laboratory)

**Abstract**: Real world data can often be high dimensional, meaning having many features. From images with thousands of pixels, to scientific data with many properties, it is often productive to reduce the "dimensionality" of your datasets in statistically meaningful ways, either before applying predictive methodologies, or simply for helping with visualization and analysis. In particular, we will focus on Principal Component Analysis, which is arguably the simplest and most well known of these methods. In this tutorial, you will learn the basics of dimensionality reduction, the theory behind it and how to apply it to a variety of scientific problems. **GitHub link**: https://github.com/matthewcarbone/AIML-tutorials