CTOs, heads of data and AI, lead data scientists, ML engineers and architects.

Probabilistic Thinking for Modern AI

• 2 days
• HRDC claimable
• Beginner

Your team leaves able to quantify uncertainty, calibrate model confidence, and govern the risk in automated decisions, bridging classical ML and generative AI.

Large firms already use AI at around 40% against 11.9% of small firms (OECD, 2025). The gap is widening fastest in the reasoning that separates a reliable model from a confident-sounding wrong one. Source: OECD, 2025

Probabilistic methods earn their keep where a wrong call is expensive. We have built forecasting and prediction systems for Malaysian enterprises, including hospitality demand models that outperformed manual forecasting. See a case study.

What your team will be able to do

• Use Bayesian reasoning for probabilistic modeling in business contexts.
• Manage uncertainty in GenAI systems through parameter control.
• Architect decision engines with Bayesian networks and Monte Carlo methods.
• Calibrate and evaluate model confidence.
• Set algorithmic risk governance under PDPA and ethical AI guidelines.

Who runs it

This program is run by industry practitioners who have shipped AI inside regulated enterprises across banking, government, manufacturing, and telco. It is led by Peter Kua, our AI and data science lead, a core contributor to Malaysia’s National Big Data Analytics Framework under MDEC and co-author of Malaysia’s AI Adoption Blueprints in 2019 and 2020. You are learning from people who have built this in production, not read about it. Peter’s commentary on AI adoption and workforce capability has appeared in The Star, theSun, Singapore Business Review, e27, and DagangNews, and on RTM Berita’s Tech Today.