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UID:20260523T005826Z - 80785@eu441a.odoo.com
DTSTART;TZID=Europe/Brussels:20260526T090000
DTEND;TZID=Europe/Brussels:20260528T130000
CREATED:20260523T005826Z
DESCRIPTION:<a href="https://www.sustain.brussels/event/from-data-to-predic
 tion-191/register">From Data to Prediction </a>\nCourse Description From D
 ata to Prediction is a practical introduction to machine learning and AI d
 esigned specifically for non-technical professionals who need to evaluate\
 , approve\, purchase\, or oversee AI solutions. Rather than teaching progr
 amming\, the course builds critical thinking skills and decision framework
 s that help participants judge whether an AI system is reliable\, fair\, a
 nd genuinely useful. Over three half-days\, participants learn to move bey
 ond marketing claims and “AI hype” by understanding core concepts\, in
 terpreting model results\, and identifying common pitfalls such as bias\, 
 overfitting\, and misleading performance metrics. Through guided exercises
 \, case studies\, and structured evaluation tools\, they will practice ask
 ing the right questions to vendors and translating technical outputs into 
 clear recommendations for leadership. By the end of the course\, attendees
  will be able to assess ML/AI proposals confidently\, communicate risks to
  stakeholders\, and make informed adoption decisions grounded in evidence 
 rather than promises. Course Content Day Content 1 Thinking Tools for Pred
 iction: Critical thinking foundations\; review of common ML/AI concepts\; 
 recognizing AI hype vs real capabilities 2 Evaluating ML Solutions: Model 
 performance metrics\; data adequacy\; overfitting & data leakage\; mismatc
 h between target and goal\; reproducibility and explainability challenges.
  3 Fairness\, Ethics & Decision: Bias sources & fairness definitions\; eva
 luation to action. Learning Outcomes Critically evaluate ML/AI vendor clai
 ms and model documentation Identify Bias\, overfitting\, and other methodo
 logical red flags in AI/ML solutions Apply structured evaluation framework
 s (grids\, checklists) to assess solution reliability and fairness Disting
 uish genuine capability from AI hype Communicate model risk assessments cl
 early to non-technical stakeholders. Practical Work [...]
DTSTAMP:20260523T005826Z
LOCATION:BeCentral\, Cantersteen 12\, 1000 Bruxelles\, Belgium
SUMMARY:From Data to Prediction 
X-ALT-DESC;FMTTYPE=text/html:<a href="https://www.sustain.brussels/event/fr
 om-data-to-prediction-191/register">From Data to Prediction </a>\nCourse D
 escription From Data to Prediction is a practical introduction to machine 
 learning and AI designed specifically for non-technical professionals who 
 need to evaluate\, approve\, purchase\, or oversee AI solutions. Rather th
 an teaching programming\, the course builds critical thinking skills and d
 ecision frameworks that help participants judge whether an AI system is re
 liable\, fair\, and genuinely useful. Over three half-days\, participants 
 learn to move beyond marketing claims and “AI hype” by understanding c
 ore concepts\, interpreting model results\, and identifying common pitfall
 s such as bias\, overfitting\, and misleading performance metrics. Through
  guided exercises\, case studies\, and structured evaluation tools\, they 
 will practice asking the right questions to vendors and translating techni
 cal outputs into clear recommendations for leadership. By the end of the c
 ourse\, attendees will be able to assess ML/AI proposals confidently\, com
 municate risks to stakeholders\, and make informed adoption decisions grou
 nded in evidence rather than promises. Course Content Day Content 1 Thinki
 ng Tools for Prediction: Critical thinking foundations\; review of common 
 ML/AI concepts\; recognizing AI hype vs real capabilities 2 Evaluating ML 
 Solutions: Model performance metrics\; data adequacy\; overfitting & data 
 leakage\; mismatch between target and goal\; reproducibility and explainab
 ility challenges. 3 Fairness\, Ethics & Decision: Bias sources & fairness 
 definitions\; evaluation to action. Learning Outcomes Critically evaluate 
 ML/AI vendor claims and model documentation Identify Bias\, overfitting\, 
 and other methodological red flags in AI/ML solutions Apply structured eva
 luation frameworks (grids\, checklists) to assess solution reliability and
  fairness Distinguish genuine capability from AI hype Communicate model ri
 sk assessments clearly to non-technical stakeholders. Practical Work [...]
END:VEVENT
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