Data Associate Programme
SMUBIA's flagship programme. A selective cohort spends a semester learning the machine learning core and shipping a real project — mentored from first idea to final showcase.
The programme
DAP is application-based. Rather than sit through lectures, associates learn the way the field is practised — in small teams, teaching the theory to one another and building a project of their own alongside it, mentors beside them the whole way.
AY 26/27 curriculum
Nine topics across one semester — each taken deep by a team and taught back to the cohort. Together they map the essentials of modern machine learning.
Modelling continuous outcomes — the linear baseline every later model is measured against.
Drawing decision boundaries, from logistic regression to margin-based classifiers.
Bagging, boosting and forests — many weak learners combined into one strong one.
Backpropagation and the building blocks that sit beneath modern deep learning.
Collaborative filtering and matrix factorisation — the logic behind what you're shown next.
Convolutional architectures that let a machine read an image.
Tokens, embeddings and representing meaning as vectors.
Attention and transformers — the architecture under today's language models.
Agents that learn by acting: reward, policy and the exploration trade-off.
How it runs
Co-learning
The surest way to learn something is to teach it. Teams of four take one topic from the curriculum deep — the mathematics and the intuition — then teach it back to the cohort. Week by week, the group builds the machine learning core together.
The project
Application first. Alongside the theory, every team proposes and builds a data project of their own — any problem, so long as it's real — mentored from proposal to working demo, and presented to the community at the end of the semester.
No prior machine learning experience needed — only commitment.
See what associates built →