Update README.md

README.md

@@ -58,25 +58,14 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha
 ![A decision tree surrogate model forms a flow chart of a more complex monotonic GBM](/img/lecture_2.png)
 <sub><sup>**Source:** [Global and Local Explanations of a Constrained Model](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_2.ipynb)</sup></sub>
 
-### Lecture 2 Suggested Reading
-
-* **Introduction and Background**: 
-  * [*Proposed Guidelines for the Responsible Use of Explainable Machine Learning*](https://arxiv.org/pdf/1906.03533.pdf)
-  * [**Responsible Artificial Intelligence**](https://www.springer.com/gp/book/9783030303709) - Chapter 4
-
-* **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)
-
-* **Links from Lecture**:
-
 ### Lecture 2 Class Materials
 
 * [Lecture Notes](tex/lecture_2.pdf) (draft)
 * [Lecture Video]() (currently unavailable)
 * Software Example: [Global and Local Explanations of a Constrained Model](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_2.ipynb) (draft)
 
 ### Lecture 2 Suggested Software
+
 * Python:
   * [`alibi`](https://github.com/SeldonIO/alibi)
   * [`interpret`](https://github.com/interpretml/interpret)
@@ -85,6 +74,20 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha
 * R:
   * [`DALEX`](https://cran.r-project.org/web/packages/DALEX/index.html)
   * [`iml`](https://cran.r-project.org/web/packages/iml/index.html)
+
+### Lecture 2 Suggested Reading
+
+* **Introduction and Background**: 
+  * [*Proposed Guidelines for the Responsible Use of Explainable Machine Learning*](https://arxiv.org/pdf/1906.03533.pdf)
+  * [**Responsible Artificial Intelligence**](https://www.springer.com/gp/book/9783030303709) - Chapter 4
+
+* **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)
+
+* **Links from Lecture**:
+
+
 
 ## Using Class Software Resources