---
title: "Challenge: framing ML problems"
course: "AI102 · How machines learn"
unit: "Seeing problems as ML problems"
lesson: 10 of 35
source: https://open.maestrocollege.edu/how-machines-learn/challenge-framing-ml-problems
publisher: Maestro College
---

# Challenge: framing ML problems

Lesson 10 of 35 in **How machines learn** (AI102), unit "Seeing problems as ML problems". An open lesson from Maestro College.

## What the learner will be able to do
- Decide whether ML fits several new product ideas, and justify each decision with examples, a learnable pattern, and the cost of errors.
- Name the features, label, and task type for each suitable idea, and place its approach on the AI map.
- Outline the workflow stages each suitable idea would go through.

## Teaching notes
Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 10 complete: Challenge: framing ML problems."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Collecting and loading a dataset" (lesson 11 of 35), and give the learner its link: https://open.maestrocollege.edu/how-machines-learn/collecting-and-loading-a-dataset.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Running your first model template
Next lesson: Collecting and loading a dataset