GWU_DNSC 6330: Course Outline

Materials for a technical, nuts-and-bolts course about increasing transparency, fairness, robustness, and security in machine learning.

• Lecture 1: Explainable Machine Learning Models
• Lecture 2: Post-hoc Explanation
• Lecture 3: Bias Testing and Remediation
• Lecture 4: Machine Learning Security
• Lecture 5: Machine Learning Model Debugging
• Lecture 6: Responsible Machine Learning Best Practices
• Lecture 7: Risk Mitigation Proposals for Language Models

Corrections or suggestions? Please file a GitHub issue.

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Preliminary Materials

• Syllabus
• Basic Data Manipulation
• Primer on Technical Malpractice
• Whiteboard Notation

Lecture 1: Explainable Machine Learning Models

_{^{Source: Simple Explainable Boosting Machine Example}}

Lecture 1 Class Materials

• Lecture Notes
• Software Example
• Assignment 1:
  • Model evaluation notebook
  • Full evaluations results
• Reading: Machine Learning for High-Risk Applications, Chapter 2 (pp. 33 - 50) and Chapter 6 (pp. 189 - 217)
  • Check availablity through GWU Libraries access to O'Reilly Safari
• Lecture 1 Additional Materials

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Lecture 2: Post-hoc Explanation

_{^{Source: Global and Local Explanations of a Constrained Model}}

Lecture 2 Class Materials

• Lecture Notes
• Software Example
• Assignment 2
• Reading: Machine Learning for High-Risk Applications, Chapter 2 (pp. 50 - 80) and Chapter 6 (pp. 208 - 230)
  • Check availablity through GWU Libraries access to O'Reilly Safari
• Lecture 2 Additional Materials

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Lecture 3: Bias Testing and Remediation

_{^{Source: Lecture 3 Notes}}

Lecture 3 Class Materials

• Lecture Notes
• Software Example
• Assignment 3
  • Model evaluation notebook
  • Full evaluations results
• Reading Machine Learning for High-Risk Applications, Chapter 4 and Chapter 10
  • Check availablity through GWU Libraries access to O'Reilly Safari
• Lecture 3 Additional Materials

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Lecture 4: Machine Learning Security

_{^{Source: Responsible Machine Learning}}

Lecture 4 Class Materials

• Lecture Notes
• Software Examples:
  • Attacks for Red-teaming
  • Data Poisoning
  • Backdoor Attack
• Assignment 4
• Reading: Machine Learning for High-Risk Applications, Chapter 5 and Chapter 11
• Lecture 4 Additional Materials

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Lecture 5: Machine Learning Model Debugging

_{^{Source: Real-World Strategies for Model Debugging}}

Lecture 5 Class Materials

• Lecture Notes
• Software Examples:
  • Sensitivity Analysis:
    • Adversarial Example Search
    • Stress Testing
  • Residual Analysis
• Assignment 5
• Reading: Machine Learning for High-Risk Applications, Chapter 3 and Chapter 8
  • Check availablity through GWU Libraries access to O'Reilly Safari
• Lecture 5 Additional Materials

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Lecture 6: Responsible Machine Learning Best Practices

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

Lecture 6 Class Materials

• Lecture Notes
• Assignment 6 (Final Assessment)
• Reading: Machine Learning for High-Risk Applications, Chapter 1 and Chapter 12
• Lecture 6 Additional Materials

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Lecture 7: Risk Mitigation Proposals for Language Models

_{^{A diagram for retrieval augmented generation. Source: Lecture 7 notes.}}

Lecture 7 Class Materials

• Lecture Notes
• Software Example
• Reading: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, pgs. 1-12, 47-53
• Lecture 7 Additional Materials