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GWU_DNSC 6290: Course Outline

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 2: Post-hoc Explanation
  • Lecture 3: Discrimination Testing and Remediation
  • Lecture 4: Machine Learning Security
  • Lecture 5: Machine Learning Model Debugging
  • Lecture 6: Responsible Machine Learning Best Practices

Corrections or suggestions? Please file a GitHub issue.


Lecture 1: Self-explainable Machine Learning Models

Histogram, partial dependence, and ICE for a monotonic GBM and a credit card customer's most recent repayment status Source: Simple Explainable Boosting Machine Example

Lecture 1 Class Materials

Lecture 1 Suggested Software

Lecture 1 Suggested Reading


Lecture 2: Post-hoc Explanation

A decision tree surrogate model forms a flow chart of a more complex monotonic GBM Source: Global and Local Explanations of a Constrained Model

Lecture 2 Class Materials

Lecture 2 Suggested Software

Lecture 2 Suggested Reading


Lecture 3: Discrimination Testing and Remediation

Two hundred neural networks from a random grid search trained on the UCI Credit Card Default dataset Source: Lecture 3 Notes

Lecture 3 Class Materials

Lecture 3 Suggested Software

Python:

Lecture 3 Suggested Reading


Lecture 4: Machine Learning Security

A cheatsheet for ML attacks Source: Responsible Machine Learning

Lecture 4 Class Materials

Lecture 4 Suggested Software

Python:

Lecture 4 Suggested Reading


Lecture 5: Machine Learning Model Debugging

Residuals for an important feature betray a serious problem in a machine learning model. Source: Real-World Strategies for Model Debugging

Lecture 5 Class Materials

Lecture 5 Suggested Software

Lecture 5 Suggested Reading


Lecture 6: Responsible Machine Learning Best Practices

A responsible machine learning workingflow

A Responsible Machine Learning Workflow Diagram. Source: Information, 11(3) (March 2020).

Lecture 6 Class Materials

Lecture 6 Suggested Software

Lecture 6 Suggested Reading


Using Class Software Resources

Mac

Preliminaries:

  • Install homebrew: $ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

  • Install Java:

brew tap adoptopenjdk/openjdk
brew cask install adoptopenjdk8
  1. Clone this repository with the examples: $ git clone https://github.com/jphall663/GWU_rml.git

  2. Install Anaconda Python 5.1.0 from the Anaconda archives (Anaconda3-5.1.0-MacOSX-x86_64.pkg) and add it to your system path. (This means when you type $ python at the command prompt, you will start a Python 3.6 session. You should probably check for a line like this export PATH="/Users/phall/anaconda/bin:$PATH" in your .bash_profile file. You may also need to close and restart your terminal session.)

  3. Install virtualenv: $ pip install virtualenv

  4. Change directories into the cloned repository: $ cd GWU_rml

  5. Create a Python 3.6 virtual environment: $ virtualenv -p /path/to/anaconda3/bin/python3.6 env_rml (/path/to/anaconda3/bin/python3.6 is not a real path. You must replace it with the path to python3.6 on your system. This can be as simple as $ virtualenv -p python3.6 env_rml.)

  6. Activate the virtual environment: $ source env_rml/bin/activate

  7. Install the correct packages for the example notebooks: $ pip install -r requirements.txt

  8. Start Jupyter: $ jupyter notebook

  9. When you are finished, deactivate the environment: $ deactivate

If you get stuck, try this link: Mac Setup: Virtualenv.

Ubuntu

  1. Install Git ($ sudo apt-get install git), Graphviz ($ sudo apt-get install graphviz) and Java ($ sudo apt-get install openjdk-8-jdk). They must all be added to your system path.

  2. Clone this repository with the examples: $ git clone https://github.com/jphall663/GWU_rml.git

  3. Install Anaconda Python 5.1.0 from the Anaconda archives (Anaconda3-5.1.0-Linux-x86_64.sh) and add it to your system path. (This means when you type $ python at the command prompt, you will start a Python 3.6 session.)

  4. Install virtualenv: $ pip install virtualenv

  5. Change directories into the cloned repository: $ cd GWU_rml

  6. Create a Python 3.6 virtual environment: $ virtualenv -p /path/to/anaconda3/bin/python3.6 env_rml

  7. Activate the virtual environment: $ source env_rml/bin/activate

  8. Install the correct packages for the example notebooks: $ pip install -r requirements.txt

  9. Start Jupyter: $ jupyter notebook

  10. When you are finished, deactivate the environment: $ deactivate.


Some materials Copyright Patrick Hall and the H2O.ai team 2017-2020.