ISO/IEC Course in Validation of Defence Algorithms
Online 300-hour course in ISO/IEC for validating defence algorithms. 6 modules covering ISO/IEC 42001 (AI management systems), ISO/IEC 23894 (AI risk management) and Python (scikit-learn). Assigned tutor and open enrolment.
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What you will be able to do
Characterise the algorithms used in defence systems.
Apply ISO/IEC standards for AI quality and risk.
Evaluate the quality and representativeness of training data.
Design performance, robustness and bias tests.
Document validation evidence in a traceable way.
Define algorithm acceptance criteria.
Assess the ethical implications of algorithmic validation.
Who it's for
- AI validation engineer who wants to specialise in ISO/IEC for validating defence algorithms.
- Data scientist who wants to specialise in ISO/IEC for validating defence algorithms.
- Machine learning engineer who wants to specialise in ISO/IEC for validating defence algorithms.
- AI systems auditor who wants to specialise in ISO/IEC for validating defence algorithms.
- Verification and validation engineer who wants to specialise in ISO/IEC for validating defence algorithms.
- Software quality assurance manager who wants to specialise in ISO/IEC for validating defence algorithms.
- Engineering or science graduates looking to enter the defence industry.
What you'll work with
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- ISO/IEC TR 24029-1 (robustness of neural networks)
- ISO/IEC 5259 (data quality for analytics and machine learning)
- ISO/IEC 25059 (quality model for AI systems)
- IEEE 1012 (verification and validation)
- ISO/IEC 22989 (AI concepts and terminology)
6 modules · 300 h
1.1Deterministic versus learned algorithms1.2Sensor fusion and target tracking1.3Kalman filters and variants1.4Automatic signal classification1.5Decision support systems1.6Autonomy and levels of automation1.7Performance and robustness requirements1.8Failure risks and consequences1.9NATO principles for responsible AI1.10Workshop: specifying classifier requirements
2.1ISO/IEC 22989: AI concepts and terminology2.2ISO/IEC 23053: framework for machine learning systems2.3ISO/IEC 23894: AI risk management2.4ISO/IEC TR 24029-1: robustness of neural networks2.5ISO/IEC 24029-2: formal methods for robustness2.6ISO/IEC 25059: quality of AI systems2.7ISO/IEC 5259: data quality for analytics2.8ISO/IEC TR 24027: bias in AI systems2.9ISO/IEC 42001 and its relation to defence2.10Practice: standards map by lifecycle phase
3.1Dataset quality requirements3.2Labelling and its verification3.3Synthetic data and simulation3.4Class imbalance and rare scenarios3.5Distribution shift3.6Partitioning and information leakage3.7Traceability and data lineage3.8Documentation: datasheets and model cards3.9Protection of classified data3.10Laboratory: quality audit of a dataset
4.1Metrics: precision, recall, ROC and calibration4.2Cross-validation and out-of-sample testing4.3Adversarial testing and perturbations4.4Formal verification of neural networks4.5Explainability: SHAP, LIME and saliency maps4.6Uncertainty quantification4.7Testing in simulation and in the real environment4.8Post-deployment monitoring4.9Meaningful human oversight4.10Laboratory: adversarial attack and robustness measurement
5.1Assurance cases and GSN notation5.2Legal review under Article 365.3Integration with DO-178C and MIL-STD-882E5.4European Defence Agency guidelines on AI5.5NATO AI Strategy of 20215.6EU AI Act and the defence exclusion5.7Independent algorithm auditing5.8Change management and retraining5.9Incident recording and learning5.10Case study: assurance case for an anomaly detector
6.1Algorithm selection and its context of use6.2Definition of requirements and acceptance criteria6.3Risk analysis according to ISO/IEC 238946.4Validation plan and test sets6.5Execution of robustness tests6.6Explainability and uncertainty analysis6.7Preparation of the assurance case6.8Regulatory compliance report6.9Written report 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. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), PyTorch, MLflow, IBM AI Fairness 360, Great Expectations, in free, academic or evaluation versions. Tests are studied through real validation campaign procedures on test sets, robustness tests against perturbations, and model acceptance reviews. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Characterise the algorithms used in defence systems.
Apply ISO/IEC standards for AI quality and risk.
Evaluate the quality and representativeness of training data.
Design performance, robustness and bias tests.
Document validation evidence in a traceable way.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory 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 totals 300 hours spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Python (scikit-learn), PyTorch, MLflow, IBM AI Fairness 360, Great Expectations. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the recognition policy when you enrol.
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