In short
Machine learning is a branch of artificial intelligence in which models learn patterns from data to make predictions or decisions, without being explicitly programmed with rules.
Key takeaways
- Supervised learning trains on labeled examples; unsupervised learning finds patterns, such as clusters, in unlabeled data.
- Biased or poor training data produces biased or poor results.
- Models need monitoring because their accuracy can drift as conditions change.
Where it's tested
ISC: Emerging Technology Considerations — study notes, a five-minute revision sheet and practice questions.
Practise: 3 questions
Question 1 of 3 · 0 answered
A machine learning model that flags suspicious expense claims becomes less accurate over two years as spending patterns change. This is called: