The course will enable the learner to apply the fundamental principles behind safety of machine learning to a wide range of applications. The course guides learners through an appropriate selection of methods and tools tailored to the learner’s specific projects. With the acquired knowledge the learner will be able to shape the development and assessment of ML-based safety-related functions enabling their teams to leverage the power of advanced ML techniques without undermining safety.

Learning Objectives

By successfully completing this course, you’ll be able to:

• Recognize the impact of machine learning (ML) on functional safety (ISO 26262) and the safety of the intended functionality (ISO 21448).
• Apply key concepts and terminology associated with machine learning relevant to the formulation of a safety assurance argument (e.g., robustness, bias, prediction certainty).
• Develop a project-specific safety-lifecycle that integrates ML-specific safety activities and safety artefacts into an overall system-level development process.
• Derive specific safety requirements for an ML-based function from a system level context.
• Analyze a given ML-based function with respect to its safety-related properties and identify insufficiencies relevant to the safety of the function.
• Know and apply established methods at design time and operation time to address insufficiencies of the most common ML techniques.

• Evaluate the effectiveness of new ML techniques and countermeasures for insufficiencies toward ensuring the safety of an ML-based function.