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Machine Learning and Emerging Technologies
Quick questions on Supervised versus unsupervised learning explained: H2 Computing
6short Q&A pairs drawn directly from our worked dot-point answer. For full context and worked exam questions, read the parent dot-point page.
What is supervised learning?Show answer
Supervised learning uses labelled data: every training example has a known correct output. The model learns to map inputs (features) to those outputs, then predicts the output for new data. It splits into two task types:
What is unsupervised learning?Show answer
Unsupervised learning uses unlabelled data - no given outputs. The model finds structure or patterns in the data itself. The main task type is:
What is reinforcement learning?Show answer
Reinforcement learning trains an agent to make a sequence of decisions by interacting with an environment. The agent takes actions, receives rewards or penalties, and learns a strategy that maximises total reward over time. Unlike supervised learning, there are no labelled correct answers - the agent learns from the consequences of its actions through trial and error (used in game-playing and robotics).
What is q1?Show answer
State the difference in the data used by supervised and unsupervised learning. [2 marks]
What is q2?Show answer
Is predicting next month's rainfall in millimetres classification or regression? Explain. [2 marks]
What is q3?Show answer
How does reinforcement learning differ from supervised learning? [1 mark]
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