Active Learning Demo

This is a basic script I used for showcasing an Active Learning approach using Gaussian Processes (GP) for a presentation on improved sampling strategies for computationally expensive functions. The actual Active Learning part is extremely simple, the next sampling point is selected based solely on the maximum standard deviation of the GP. In previous roles I have implemented more robust approaches that better balance exploration versus exploitation such as using maximum entropy or the Mismatch-first farthest-traversal heuristic to select the next sampling point. Included in this repo is a simple video I made using imagemagick and the outputs of the active_learning.py script.