ph_ (jphall663)

jphall663

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Location:Washington, DC

Home Page:hallresearch.ai

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the-george-washington-university

ph_'s repositories

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.

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GWU_data_mining

Materials for GWU DNSC 6279 and DNSC 6290.

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secure_ML_ideas

Practical ideas on securing machine learning models

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xai_misconceptions

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

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diabetes_use_case

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

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hc_ml

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

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kdd_2019

Paper and talk from KDD 2019 XAI Workshop

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responsible_xai

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

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jsm_2018_slides

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

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jsm_2018_paper

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

corr_graph

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

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h2oworld_sf_2019

Human-Centered ML Presentation for H2O World SF 2019.

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ds_interview_qs

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

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basic_data_viz_rules_and_links

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

ds_quick_refs

An Aggregation of a Few Decent Data Science Quick references

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jsm_2019

Slides for JSM 2019 preso on model debugging strategies

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lime_xgboost

Simple package for creating LIMEs for XGBoost

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nafsa_2018_slides

Slides for presentation at NAFSA retreat

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keep_the_science_in_data_science

Essay about science and data "science".

automl_resources

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

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bellarmine_py_intro

Code and materials for Python intro. course.

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GWU_DNSC_6301_project

Example project for DNSC 6301

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mli-resources

Machine Learning Interpretability Resources

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aequitas

Bias and Fairness Audit Toolkit

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enlighten-apply

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

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xgb_random_grid_search_example

Example code and data for XGBoost random grid search.

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DNSC-6301-Project_Group-21

DNSC 6301 Project_Group 21

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Hello-world

My first repository on GitHub.

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