Computer Science

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

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

Approaches to training machine learning models

Introduction to AI

Machine learning

Predictive vs generative AI

Social and ethical issues of AI systems

Data driven models

Classification

Machine learning engines

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.

Data science: AI and machine learning

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

Which of these is a type of supervised learning?

What is the main characteristic of unsupervised learning?

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

teaching a model with labelled animal photos

finding hidden patterns in unlabelled data

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 only works with unlabelled data.

Which statement about supervised learning is correct?

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

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

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

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

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

data collected from only one type of person

Which of these is an example of bias in data?

This lesson will be removed by end of Summer Term 2025.

Approaches to training machine learning models

Lesson outcome

This lesson will be removed by end of Summer Term 2025.