Skip to content
Oak National Academy
Oak National Academy
View my library
0
Teachers
Pupils
View unit
New
New
Lesson 6 of 8
  • Year 10
  • OCR

Approaches to training machine learning models

I can explain the difference between supervised and unsupervised machine learning models.

Download all resources
Share activities with pupils
View unit
Lesson 6 of 8
New
New
  • Year 10
  • OCR

Approaches to training machine learning models

I can explain the difference between supervised and unsupervised machine learning models.

Download all resources
Share activities with pupils

These resources will be removed by end of Summer Term 2025.

Switch to our new teaching resources now - designed by teachers and leading subject experts, and tested in classrooms.

These resources were created for remote use during the pandemic and are not designed for classroom teaching.

View new resources

Lesson details

Key learning points

  1. Supervised learning approaches use large amounts of data labelled by people with relevant information.
  2. One type of supervised learning is classification.
  3. Machine learning developers train unsupervised learning models to organise data based on similarities.
  4. One type of unsupervised learning is clustering.

Keywords

  • Supervised learning - a form of machine learning where the model is trained using labelled data

  • Unsupervised learning - a form of machine learning where the model is trained on an unlabelled data set — the model is designed to detect patterns, hidden relationships or structures within the data

Common misconception

In supervised learning, the model stores or "memorises" the training data to label new, unprocessed data.

A supervised learning model does not simply store or "memorise" training data. It detects patterns and relationships in the training data and stores these. If it only stored the training data, it could not accurately label new, unprocessed data.


To help you plan your year 10 computer science lesson on: Approaches to training machine learning models, download all teaching resources for free and adapt to suit your pupils' needs...

This lesson introduces the concept of classification to pupils. Classification is covered in more detail in the subsequent lesson.
Teacher tip

Equipment

Licence

This content is © Oak National Academy Limited (2025), licensed on Open Government Licence version 3.0 except where otherwise stated. See Oak's terms & conditions (Collection 2).

Lesson video

Skip lesson video

Loading...

Prior knowledge starter quiz

Download quiz pdf

6 Questions

Q1.
Put these steps in order for preparing data for an AI model:

1 - collect raw data
2 - clean the data
3 - check for bias
4 - use the data to train the model

Q2.
What is the main purpose of a data-driven model?

to create artwork
Correct answer: to find patterns in data
to make music
to store passwords

Q3.
Why is it important for data to be accurate when training an AI model?

Correct answer: It ensures the results are reliable.
It makes the computer run faster.
It uses less electricity.
It looks more colourful.

Q4.
What is the term for fixing errors in a data set?

Correct Answer: cleaning, data cleaning

Q5.
What is one reason data needs to be cleaned before use?

to make it colourful
to make it larger
to increase download speed
Correct answer: to remove errors and duplicates

Q6.
Which of these is an example of bias in data?

data from many different groups
data that is always correct
data that is well-organised
Correct answer: data collected from only one type of person

Assessment exit quiz

Download quiz pdf

6 Questions

Q1.
What type of machine learning uses labelled data to train a model?

Correct Answer: supervised, supervised learning

Q2.
Which of these is a type of supervised learning?

clustering
Correct answer: classification
data cleaning
sorting

Q3.
What is the main characteristic of unsupervised learning?

only works with numbers
uses labelled data
Correct answer: uses unlabelled data
requires no patterns

Q4.
What is the name for the process in unsupervised learning where similar data points are grouped together?

Correct Answer: clustering

Q5.
Match each example to the correct term:

Correct Answer:classification,sorting emails as spam or not spam

sorting emails as spam or not spam

Correct Answer:clustering,grouping customers by shopping habits

grouping customers by shopping habits

Correct Answer:supervised learning,teaching a model with labelled animal photos

teaching a model with labelled animal photos

Correct Answer:unsupervised learning,finding hidden patterns in unlabelled data

finding hidden patterns in unlabelled data

Q6.
Which statement about supervised learning is correct?

Correct answer: The model detects patterns and relationships to use with new, unseen data.
The model stores all the training data and cannot handle new information.
The model ignores the training data.
The model only works with unlabelled data.