# Active Learning Demo

![Active-Learning Demo using Gaussian Processes](./active_learning.gif)

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.

**Notes**

- 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 graphic I made using `imagemagick` and the outputs of the `active_learning.py` script.