Testing Machine Learning Models

Overview

Explore techniques for testing ML/AI models beyond metrics, focusing on behaviors, usability, and fairness. Learn to identify risks, biases, and apply user-centric testing strategies.

Syllabus

• Introduction to Testing Machine Learning Models
Overview of common ML testing metrics
Importance of testing beyond metrics
• Understanding Model Behaviors
Analyzing model predictions
Identifying unexpected behaviors
Case studies of behavioral failures in ML
• Usability Testing for Machine Learning Models
User experience and interaction with ML systems
Techniques for user-centric assessment
Designing effective usability tests
• Bias and Fairness in Machine Learning
Types of biases in ML models
Tools and techniques for bias detection
Methods to ensure fairness
• Risk Identification and Mitigation
Common risks in ML models
Assessing and prioritizing risks
Strategies for risk mitigation
• User-Centric Testing Strategies
Incorporating user feedback into testing
Developing user personas for testing
Cross-disciplinary testing approaches
• Evaluation Frameworks and Case Studies
Review of existing evaluation frameworks
Analyzing real-world examples
Lessons learned from case studies
• Practical Tools and Techniques
Overview of testing tools for ML
Hands-on practice with selected tools
Best practices for testing implementation
• Capstone Project
Design a comprehensive testing plan for a given ML model
Apply learned techniques to assess behaviors, usability, and fairness
Present findings and improvement suggestions
• Wrap-up and Course Evaluation
Recap of key learnings
Open discussion on emerging challenges in ML testing
Feedback and course reflection

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Subjects

Conference Talks