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README.md

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 # 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.
-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.
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+**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.
+