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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

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* **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)
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