Year 10 - Computing
Approaches to training machine learning models
Data science: AI and machine learning
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This pupil-friendly study guide includes a unit summary, clear notes, common mistakes, and quick questions with answers.
Unit Summary
This unit introduces pupils to the emerging field of data science. They will explore what AI systems are and the different categories of AI. The concept of machine learning and how models are driven by data will be explored. They will also consider the ethical and wider implications of AI systems.
Lesson Summary
You will learn to explain the difference between supervised and unsupervised machine learning models.
Key Notes
- 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.
Vocabulary To Learn
- 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 Mistakes To Avoid
- In supervised learning, the model stores or "memorises" the training data to label new, unprocessed data.
3 Quick Questions (With Answers)
1. Describe the system or process from this lesson in clear steps.
Supervised learning approaches use large amounts of data labelled by people with relevant information. One type of supervised learning is classification.
2. Define this computing term and give one practical example. 'supervised learning'
a form of machine learning where the model is trained using labelled data. Add one real device or system example to prove understanding.
3. Correct this common computing misconception.
Mistake: In supervised learning, the model stores or "memorises" the training data to label new, unprocessed data. Correction: 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.
More Lessons In This Unit
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