Update am1.md

additional_materials/am1.md

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 * [Building from Penalized GLM to Monotonic GBM (simple)](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_1.ipynb?flush_cache=true)
 * [Building from Penalized GLM to Monotonic GBM](https://nbviewer.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/glm_mgbm_gbm.ipynb?flush_cache=true)
-* [Simple Explainable Boosting Machine Example](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_1_ebm_example.ipynb?flush_cache=true)
+* [Simple Explainable Boosting Machine Example](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/additional_materials/lecture_1_ebm_example.ipynb?flush_cache=true)
 * [PiML Assignment 1 Example](https://github.com/jphall663/GWU_rml/blob/master/assignments/assignment_1/group6_PiML_example.ipynb) and simple [requirements.txt](https://github.com/jphall663/GWU_rml/blob/master/assignments/assignment_1/piml_requirements.txt)
 * _Machine Learning for High-risk Applications_: [Use Cases](https://oreil.ly/machine-learning-high-risk-apps-code) (Chapter 6)