Merge branch 'master' of https://github.com/jphall663/GWU_rml

README.md

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 Materials for a technical, nuts-and-bolts course about increasing transparency, fairness, security and privacy in machine learning.
 
-* Lecture 1: Self-explainable Machine Learning Models
+* Lecture 1: Explainable Machine Learning Models
 * Lecture 2: Post-hoc Explanation
 * Lecture 3: Discrimination Testing and Remediation
 * Lecture 4: Machine Learning Security
@@ -13,7 +13,7 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha
 
 ***
 
-## Lecture 1: Self-explainable Machine Learning Models
+## Lecture 1: Explainable Machine Learning Models
 
 ![Histogram, partial dependence, and ICE for a monotonic GBM and a credit card customer's most recent repayment status](/img/ebm.png)
 <sub><sup>**Source:** [Simple Explainable Boosting Machine Example](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_1_ebm_example.ipynb?flush_cache=true) (Jupyter)</sup></sub>
@@ -26,7 +26,7 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha
   * [Primer on Technical Malpractice](https://docs.google.com/presentation/d/1cZeaoIp4cQsVY_gj2a5Pg7ygexepQZRS-ZEn6n2QqEU/edit?usp=sharing)
   * [Whiteboard Notation](https://docs.google.com/presentation/d/1Axf9dizaE3XvGRelBHfsnAlMUPFuMExQ2WNVwQBKMrw/edit?usp=sharing)
 * [Lecture Notes](tex/lecture_1.pdf)
-* [Assignment 1](https://raw.githubusercontent.com/jphall663/GWU_rml/master/assignments/tex/assignment_1.pdf) 
+* [Assignment 1](assignments/tex/assignment_1.pdf) 
   * [Model evaluation notebook](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/assignments/eval.ipynb)
   * [Full evaluations results](https://github.com/jphall663/GWU_rml/blob/master/assignments/model_eval_2022_06_13_09_47_08.csv)
 * Software Examples: