This four-day course provides a comprehensive introduction to Artificial Intelligence (AI) and its application in modern systems. Participants will explore the foundational concepts of AI, including its types, technologies, and development [...]
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This four-day course provides a comprehensive introduction to Artificial Intelligence (AI) and its application in modern systems. Participants will explore the foundational concepts of AI, including its types, technologies, and development frameworks, as well as the unique quality characteristics that distinguish AI-based systems—such as autonomy, adaptability, ethics, and transparency. The course also covers the essentials of Machine Learning (ML), from algorithm selection and data preparation to performance metrics and neural networks, equipping learners with a solid understanding of how ML models are developed and evaluated.Building on this foundation, the course delves into the challenges and methodologies of testing AI-based systems. Learners will examine test strategies for AI-specific traits like bias, non-determinism, and concept drift, and gain hands-on insight into techniques such as adversarial testing, metamorphic testing, and A/B testing. The final sessions focus on test environments and the use of AI to enhance software testing processes, including defect analysis and regression optimization. By the end of the course, participants will be equipped to critically assess, test, and apply AI technologies in real-world scenarios.

  • Understand the foundational concepts of Artificial Intelligence, including its types, technologies, and development frameworks.
  • Explore the quality characteristics specific to AI-based systems, such as adaptability, autonomy, ethics, and transparency.
  • Gain a comprehensive overview of Machine Learning (ML), including its forms, workflows, and algorithm selection criteria.
  • Learn the importance of data in ML, including data preparation, dataset types, and the impact of data quality on model performance.
  • Evaluate ML performance using functional metrics and benchmark suites for classification, regression, and clustering tasks.
  • Understand neural networks and their testing methodologies, including coverage measures and concept drift.
  • Apply testing strategies tailored to AI-based systems, addressing specification, test levels, and automation bias.
  • Examine challenges in testing AI-specific quality traits, such as bias, non-determinism, and explainability.
  • Explore various testing techniques for AI systems, including adversarial testing, metamorphic testing, and A/B testing.
  • Discover how AI can be leveraged to enhance software testing processes, including defect analysis, test case generation, and regression optimization.

Mám záujem o vybraný QA kurz