Computer Science

machine learning algorithms use data to identify patterns and make guesses about the future

a type of artificial intelligence (AI) designed to generate content, such as text, images or sound

Predictive vs generative AI

Introduction to AI

Machine learning

Social and ethical issues of AI systems

Data driven models

Approaches to training machine learning 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 do we call AI that produces new and original content?

Which task would most likely use predictive AI?

What is the main function of generative AI?

Which of these is an example of using predictive AI?

Arrange these steps in the correct order for using predictive AI:

both types of AI identify patterns in data

Which of the following statements is false?

What is the name for an approach in artificial intelligence where systems improve by learning from data?

an approach where systems learn from data

rule-based systems use data to learn rules

rule-based systems follow predefined rules; data-driven systems learn from data

What is the main difference between rule-based and data-driven systems?

Why might a machine learning model need to be retrained?

Place these steps in order for developing a data-driven AI system:

How can a data-driven system become more accurate?

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

Predictive vs generative AI

Lesson outcome

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