Training Track: From Data to Prediction
Course Description
This course gives non-technical decision-makers the skills to evaluate AI and machine learning tools.
Participants learn to challenge vendor claims, spot bias, and judge whether a prediction or solution can
be trusted, using simple frameworks and the right questions to cut through the hype.
Course Content
Day | Content |
1 | Thinking Tools for Prediction: Critical thinking foundations. Basic AI and machine learning literacy. The science behind AI tools. |
2 | The evaluation grid, part 1 : Key concepts and case studies: consistency, robustness, quality of abstention, predictability, accuracy, confidence, safety, compliance, severity of harm, human override, transparency |
3 | The evaluation grid, part 2, and current limits of AI: Performance metrics, data quality, overfitting and data leakage, gaps between what a model predicts and what you actually want, reproducibility. Explainability and interpretability, bias and fairness, dependency, accountability, privacy. |
Learning Outcomes
By the end of the course, participants can:
- Question vendor claims and read model documentation with a critical eye
- Spot bias, overfitting, and other warning signs in AI solutions
- Use grids and checklists to judge a solution's reliability and fairness
- Tell real capability from hype, and match the right type of AI to the job
- Explain the risks of a model in plain language to colleagues and decision-makers
Practical Work
- Thought experiments
- Evaluation grid walkthrough
- Audit case study; evaluation memo drafting
Deliverables
- ML Solution Evaluation Grid — structured assessment grid across accuracy, fairness, transparency, etc.
- Red Flags Checklist — reusable tool for evaluating ML/AI proposal
- Model Evaluation Memo — one-page non-technical recommendation for senior leadership
Target Audience:
Non-technical professionals from public or private organisations, including managers, procurement
officers, project coordinators, innovation officers, and any non-technical professional involved in
evaluating, purchasing, or overseeing ML/AI solutions.
Trainer
Natalia Garcia Colin is an experienced research scientist and technical leader specializing in AI, ML, and mathematical modelling, with proven success managing international projects and communicating complex concepts to diverse audiences in academia and industry. Throughout her career, she has taught undergraduate and master's courses across engineering, mathematics, economics, data science, and financial engineering. Her work bridges technical innovation with education while fostering global collaborative networks.
Price
Thanks to the support of the European Commission and Innoviris in the framework of the
EDIH sustAIn.brussels, SMEs and mid-caps receive this training free of charge (0€), in the context of
de minimis aid. Large companies and participants without a company pay 960€ per participant.
Practical Information:
Language : English (Bilingual exchanges FR/EN welcome)
Location: BeCentral, Cantersteen 12, 1000 Brussels.
Format: In person, interactive, hands-on.
Participants: Max 20 participants.
Dates : 23, 24, 25 Nov 2026
Duration: 12 hours (3 days, 09:00–13:00)
Questions:
Yavuz Sarikaya - Programme Manager @ULB