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awesome-machine-learning-interpretability. A curated list of awesome responsible machine learning resources.

interpretable_machine_learning_with_python. Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

GWU_data_mining. Materials for GWU DNSC 6279 and DNSC 6290.

GWU_rml. Jupyter Notebook

secure_ML_ideas. Practical ideas on securing machine learning models

xai_misconceptions. Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!

diabetes_use_case. Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

hc_ml. Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

gai_risk_management. A place for ideas and drafts related to GAI risk management.

kdd_2019. Paper and talk from KDD 2019 XAI Workshop

responsible_xai. Guidelines for the responsible use of explainable AI and machine learning.

jsm_2018_slides. Slides for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

h2oworld_sf_2019. Human-Centered ML Presentation for H2O World SF 2019.

corr_graph. Short example for creating a correlation graph with Pandas and Gephi.

jsm_2018_paper. Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

ds_interview_qs. Some Data Science Interview Questions (by Me and Former Colleagues at SAS)

jsm_2019. Slides for JSM 2019 preso on model debugging strategies

lime_xgboost. Simple package for creating LIMEs for XGBoost

nafsa_2018_slides. Slides for presentation at NAFSA retreat

ds_quick_refs. An Aggregation of a Few Decent Data Science Quick references

basic_data_viz_rules_and_links. Some Basic (Hopefully Not Terrible) Data Visualization Rules and Links

bellarmine_py_intro. Code and materials for Python intro. course.

keep_the_science_in_data_science. Essay about science and data "science".

py_chunks. Example code and materials to a chunk and process a file using Python mulitprocessing.

GWU_DNSC_6301_project. Example project for DNSC 6301

automl_resources. A running list of links for AutoML - very unofficial and incomplete

mli-resources. Machine Learning Interpretability Resources

enlighten-deep. Example code and materials that illustrate using neural networks with several hidden layers in SAS.

enlighten-integration. Example code and materials that illustrate techniques for integrating SAS with popular open source analytics technologies like Python and R.

xgb_random_grid_search_example. Example code and data for XGBoost random grid search.

enlighten-apply. Example code and materials that illustrate applications of SAS machine learning techniques.

aequitas. Bias and Fairness Audit Toolkit

frank_not_frank. Original, free sample images.

r_gbm_samples. Some Basic Samples of Using R and H2O to Train Gradient Boosting Machines