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 ### Lecture 2 Class Materials
 
 * [Lecture Notes](tex/lecture_2.pdf) 
-* [Lecture Video](https://www.youtube.com/watch?v=AYomYeLTGkw)
+* [Lecture Video](https://www.youtube.com/watch?v=29wcG5wlRDw)
 * Software Example: [Global and Local Explanations of a Constrained Model](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_2.ipynb) 
 ### Lecture 2 Suggested Software
 
@@ -83,7 +83,7 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha
   
 * **Post-hoc Explanation Techniques**:
   * **Interpretable Machine Learning** - [Chapter 5](https://christophm.github.io/interpretable-ml-book/agnostic.html) and [Chapter 6](https://christophm.github.io/interpretable-ml-book/example-based.html)
-  * [*Towards a Better Understanding of Gradient-based Attribution Methods for Deep Neural Networks*](https://arxiv.org/pdf/1711.06104.pdf)
+  * [*Towards Better Understanding of Gradient-based Attribution Methods for Deep Neural Networks*](https://arxiv.org/pdf/1711.06104.pdf)
 
 * **Links from Lecture**:
   * [On the Art and Science of Explainable Machine Learning](https://arxiv.org/pdf/1810.02909.pdf)