Study Guides

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 explore a range of machine learning engines which are used to build AI models.

Key Notes

• The engines of machine learning are the data structures and algorithms that are used to create a model.
• The engine you choose will depend on the nature of the problem you want to solve and the characteristics of your data.
• A decision tree is a supervised machine learning algorithm.
• Machine learning developers use decision trees to structure a set of conditions, which can be used to make predictions.
• A neural network is a type of machine learning model inspired by the human brain.

Vocabulary To Learn

• decision tree: a type of ML engine that makes predictions by splitting data into branches using conditions
• neural network: a type of ML engine consisting of interconnected nodes, organised in layers, that is trained to find patterns in data
• image generator: an AI tool that produces pictures from a prompt using patterns it has identified in large collections of existing images

Common Mistakes To Avoid

• Neural networks offer transparency in terms of the decisions made by the algorithm.

3 Quick Questions (With Answers)

More Lessons In This Unit

• Approaches to training machine learning models Study Guide
• Classification Study Guide
• Data driven models Study Guide
• Introduction to AI Study Guide
• Machine learning Study Guide
• Predictive vs generative AI Study Guide
• Social and ethical issues of AI systems Study Guide