Course in International Standards for Critical AI
Online 300 h course in international standards for critical AI. 6 modules covering ISO/PAS 8800 (AI in road vehicles), Regulation (EU) 2024/1689 (Artificial Intelligence Act) and Python (PyTorch). Assigned tutor and open enrolment.
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What you will be able to do
Identify the challenges of integrating AI into safety-critical systems.
Interpret horizontal AI standards and their relationship with functional safety.
Analyse AI certification approaches in aviation and space.
Compare sector requirements in automotive, rail and healthcare.
Apply verification and validation techniques to machine learning models.
Assess the robustness and explainability of an AI component.
Structure an assurance case for a system with AI.
Who it's for
- AI assurance engineer wishing to specialise in International Standards for Critical AI.
- Functional safety engineer wishing to specialise in International Standards for Critical AI.
- AI verification and validation specialist wishing to specialise in International Standards for Critical AI.
- Software certification manager wishing to specialise in International Standards for Critical AI.
- Critical systems engineer wishing to specialise in International Standards for Critical AI.
- AI compliance consultant wishing to specialise in International Standards for Critical AI.
- Graduates in engineering or sciences seeking to enter the aerospace and space industry.
What you'll work with
- ISO/PAS 8800 (AI in road vehicles)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/IEC 42001 (AI management systems)
- ISO 21448 (SOTIF)
- DO-178C (airborne software)
- IEC 62304 (medical device software)
- EN 50716 (railway software)
6 modules · 300 h
1.1What makes a system critical1.2Sectors: aviation, rail, medical, energy1.3Limitations of machine learning1.4Non-determinism and generalisation1.5Failure modes of neural networks1.6Adversarial examples and robustness1.7Data drift in operation1.8Certification versus learning1.9Map of international standards1.10Workshop: hazard identification for an AI system
2.1ISO/IEC 22989 on concepts and terminology2.2ISO/IEC 23053 on the machine learning framework2.3ISO/IEC 23894 on risk management2.4ISO/IEC 42001 on management systems2.5ISO/IEC TR 24029 on robustness of neural networks2.6ISO/IEC TR 5469 on AI and functional safety2.7ISO/IEC 25059 on quality of AI systems2.8CEN-CENELEC JTC 21 work2.9NIST AI Risk Management Framework2.10Practice: gap analysis against ISO/IEC 42001
3.1EASA guidance on AI level 1 and 23.2EASA concept paper on ML3.3SAE G-34 and EUROCAE WG-1143.4ARP6983 under development3.5Learning assurance3.6Operational design domain3.7Relationship with DO-178C and DO-2543.8Runtime monitoring3.9FAA programmes3.10Case study: computer vision for landing
4.1ISO 26262 and its limits with AI4.2ISO 21448 SOTIF4.3ISO/PAS 8800 on AI in vehicles4.4UL 4600 for autonomous vehicles4.5AI in rail and EN 507164.6Regulation on medical devices and software4.7IEC 62304 and FDA on ML4.8Predetermined change control plan4.9Interrelation with the EU AI Act4.10Case study: perception in an autonomous vehicle
5.1Formal verification of neural networks5.2Scenario-based testing5.3Neuron and data coverage5.4Uncertainty metrics5.5Architectures with safety monitors5.6Safety arguments with GSN5.7AMLAS assurance case5.8Training data management5.9Post-deployment monitoring5.10Laboratory: robustness of a network against perturbations
6.1Description of the critical system6.2Operational domain6.3Hazard analysis6.4AI safety requirements6.5Data strategy6.6Verification plan6.7Safety argument6.8Operation and monitoring plan6.9Report and presentation6.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (PyTorch), MATLAB/Simulink, CARLA, LDRA, in free, academic or evaluation versions. The tests are studied using real verification procedures for machine learning models, the development of an assurance case, and robustness analysis against out-of-distribution data. Assessment is continuous per module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the challenges of integrating AI into safety-critical systems.
Interpret horizontal AI standards and their relationship with functional safety.
Analyse AI certification approaches in aviation and space.
Compare sector requirements in automotive, rail and healthcare.
Apply verification and validation techniques to machine learning models.
Where this takes you
Requirements
- No prior degree required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical standards reading.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme consists of 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The course works with Python (PyTorch), MATLAB/Simulink, CARLA and LDRA. Free, academic or evaluation versions are used, and the tutor indicates the free alternative when one exists.
An own SEIUM diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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