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CT-AI — AI Testing

ISTQB · Topic 3 of 17

Machine Learning Overview

Forms of machine learning (supervised, unsupervised, reinforcement). The end-to-end ML workflow from data to deployment.

6 lessons240 questions
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What this topic covers

  1. 01AI-Specific Quality Characteristics and Testing Techniques480m
  2. 02Follow-up test cases and functional correctness in ML480m
  3. 03Macro- and Micro-averaging for Multi-class Metrics480m
  4. 04Oracle Problem, Quality Metrics, and Safety in AI Testing480m
  5. 05Source test case, specificity, and transparency in AI testing480m
  6. 06Root Mean Squared Error and Adjusted R-squared for Regression Models480m

Other topics in CT-AI — AI Testing

Introduction to Artificial Intelligence7 lessonsIntroduction to AI6 lessonsInput Data Testing6 lessonsML Data6 lessonsMachine Learning Model Testing7 lessonsML Functional Performance Metrics6 lessonsMachine Learning Functional Performance Metrics6 lessonsTesting Generative AI Systems6 lessonsQuality Characteristics for AI-Based Systems4 lessonsTesting AI-Specific Quality Characteristics6 lessonsML Neural Networks and Testing6 lessonsTesting AI-Based Systems Overview6 lessonsTest Environments for AI-Based Systems6 lessonsMachine Learning Development Testing3 lessonsUsing AI for Testing6 lessonsMethods and Techniques for Testing AI6 lessons