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aequitas

Bias and Fairness Audit Toolkit

automl_resources

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

awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

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.

corr_graph

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

diabetes_use_case

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

dnsc-6301-project_group-21

DNSC 6301 Project_Group 21

ds_interview_qs

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

ds_quick_refs

An Aggregation of a Few Decent Data Science Quick references

enlighten-apply

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

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.

frank_not_frank

Original, free sample images.

gwu_data_mining

Materials for GWU DNSC 6279 and DNSC 6290.

gwu_dnsc_6301_project

Example project for DNSC 6301

gwu_rml

h2oworld_sf_2019

Human-Centered ML Presentation for H2O World SF 2019.

hc_ml

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

hello-world

My first repository on GitHub.

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.

jsm_2018_paper

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

jsm_2018_slides

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

jsm_2019

Slides for JSM 2019 preso on model debugging strategies

kdd_2019

Paper and talk from KDD 2019 XAI Workshop

keep_the_science_in_data_science

Essay about science and data "science".

lime_xgboost

Simple package for creating LIMEs for XGBoost

mli-resources

Machine Learning Interpretability Resources

nafsa_2018_slides

Slides for presentation at NAFSA retreat

py_chunks

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