Approaches to training machine learning models
Lesson details
Learning outcome
I can explain the difference between supervised and unsupervised machine learning models.
Key learning points
- Supervised learning approaches use large amounts of data labelled by people with relevant information.
- One type of supervised learning is classification.
- Machine learning developers train unsupervised learning models to organise data based on similarities.
- 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.
Teacher tip
This lesson introduces the concept of classification to pupils. Classification is covered in more detail in the subsequent lesson.
Licence
Lesson video
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Prior knowledge starter quiz
6 Questions
Q1.Put these steps in order for preparing data for an AI model:
Q2.What is the main purpose of a data-driven model?
Q3.Why is it important for data to be accurate when training an AI model?
Q4.What is the term for fixing errors in a data set?
Q5.What is one reason data needs to be cleaned before use?
Q6.Which of these is an example of bias in data?
Assessment exit quiz
6 Questions
Q1.What type of machine learning uses labelled data to train a model?
Q2.Which of these is a type of supervised learning?
Q3.What is the main characteristic of unsupervised learning?
Q4.What is the name for the process in unsupervised learning where similar data points are grouped together?
Q5.Match each example to the correct term:
sorting emails as spam or not spam
grouping customers by shopping habits
teaching a model with labelled animal photos
finding hidden patterns in unlabelled data
Q6.Which statement about supervised learning is correct?
To help you plan your 10 computer science lesson on: Approaches to training machine learning models, download all teaching resources for free and adapt to suit your pupils' needs...
To help you plan your 10 computer science lesson on: Approaches to training machine learning models, download all teaching resources for free and adapt to suit your pupils' needs.
The starter quiz will activate and check your pupils' prior knowledge, with versions available both with and without answers in PDF format.
We use learning cycles to break down learning into key concepts or ideas linked to the learning outcome. Each learning cycle features explanations with checks for understanding and practice tasks with feedback. All of this is found in our slide decks, ready for you to download and edit. The practice tasks are also available as printable worksheets and some lessons have additional materials with extra material you might need for teaching the lesson.
The assessment exit quiz will test your pupils' understanding of the key learning points.
Our video is a tool for planning, showing how other teachers might teach the lesson, offering helpful tips, modelled explanations and inspiration for your own delivery in the classroom. Plus, you can set it as homework or revision for pupils and keep their learning on track by sharing an online pupil version of this lesson.
Explore more key stage 4 computer science lessons from the Data science: AI and machine learning unit, dive into the full secondary computer science curriculum, or learn more about lesson planning.