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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