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Challenge instructions

Instruction and application
In Progress

By the end of the hackathon, each team will deliver a 5-minute presentation, followed by3 minutes of questions, covering:

  • Your proposed AI/ML solution.
  • The key data, methodology, and modelling choices that support it.
  • Governance, ethics, and stakeholder considerations.
  • Clear recommendations for next steps to take the solution forward.

Success criteria​

  • A solution that is clear, coherent, and realistic for the problem you framed.
  • Explicit assumptions, constraints, and trade-offs, clearly explained.
  • Evidence that feedback has been used to refine your approach.
  • A presentation that prioritises clarity and impact, rather than maximum detail.

Today, you'll work on framing your ML problem, defining your solution scope, and creating a first-pass technical design.

Hackathon Resource Pack​

If you haven't already, or if you need to re-download it, download the Hackathon Resource Pack now. It contains:

  • Descriptions of the two scenarios.
  • Datasets for each scenario.
  • Presentation template to capture your thinking throughout the hackathon.

You'll continue using this pack across both workshops 1 and 2.

AIMLF Hackathon Resource Pack.zip

Once downloaded, open the files and keep them accessible for the rest of the hackathon workshop.

### Instructions

Frame the ML problem​

Using the scenario description and what you observe in thedataset, frame an ML problem in your presentation template:

  • Business objective: Clearly state the business goal you are trying to achieve.
  • Target variable ($y$): Identify what you would aim to predict or estimate.
  • Modelling type: Decide whether this is best framed as classification, regression, forecasting, or another ML task.
  • Potential features ($X$): List the data fields that could act as signals for your prediction.
  • Success metric: Define how success would be measured and how it links back to the business need.

You can review the Module 1 content on framing business problems for ML solutions to support your thinking in this step.

Define solution direction and scope​

In this step, your goal is to agree on a clear, shared direction before moving into technical thinking. As a team, agree on:

  • The specific part of the problem your solution will address.
  • The AI/ML approach you are proposing at a high level.
  • What is in scope andout of scope for today.
  • The success criteria you will use to judge your solution.
  • Any key assumptions or constraints shaping your design.

If you need a refresher, review the Module 2 content on translating business needs into technical specifications to help clarify scope, assumptions, and success criteria for your solution.

Initial data, modelling, and evaluation thinking​

With a solution direction agreed, begin first-pass technical design. As a team, sketch out:

  • DataWhich data will be used and where it comes from.
  • Any obvious limitations or assumptions.
  • Pre-processing / feature engineeringWhat preparation or transformations may be required.
  • Which features are likely to be the strongest signals.
  • ModellingOne or more suitable model options.
  • Why these models fit the problem and constraints.
  • EvaluationHow success would be measured.
  • How performance would be assessed at a high level.

You can review the following learning content to support your work for this step:

Next steps in the hackathon​

In the next hackathon workshop, you'll build on this foundation by refining your data and modelling decisions, defining how success will be evaluated, and considering governance, ethical, and practical implications.

In progress