A2 · Practice questions · Machine learning: supervised, unsupervised and reinforcement

Pick the right learner.

Six original Cambridge-style questions on machine learning categories and regression, with worked solutions.

Original questions All questions on this page are original work, written in the Cambridge AS & A Level style. They are not from past papers. They test the same concepts and skills the syllabus rewards.

Keep these straight

Start from the data.

Labelled data: supervised (classification for categories, regression for numbers).
Unlabelled data: unsupervised (clustering, associations).
Score actions, no data set: reinforcement (agent, environment, reward, policy).
Regression: m = Σ(x − x̄)(y − ȳ) ÷ Σ(x − x̄)², c = ȳ − m x̄.

Recall

[2 marks]

State what is meant by machine learning.

A type of AI in which a program learns from data / experience. ✓
So it improves its performance at a task without being explicitly programmed with the rules. ✓

Explain

[4 marks]

Explain the difference between supervised learning and unsupervised learning. Give an example application of each.

Supervised learning uses labelled training data: each input has the correct output. ✓
The model learns to predict the output for new inputs, e.g. classifying emails as spam or not spam / predicting prices. ✓
Unsupervised learning uses unlabelled data, so no correct output is given. ✓
The model finds patterns or groups itself, e.g. clustering customers by buying habits. ✓

Explain

[4 marks]

Describe how reinforcement learning is used to train a robot to walk.

The robot is the agent; the floor and its body physics are the environment. ✓
In each state the robot chooses an action (e.g. move a joint) by trial and error. ✓
It receives a reward for progress (e.g. moving forward) and a penalty for falling. ✓
Over many attempts it updates its policy to choose actions that maximise its total reward. ✓

Calculate

[4 marks]

A company records the years of experience and salary (in thousands) of four employees.

Years, x	2	4	6	8
Salary, y	30	34	41	47

Calculate the least squares regression line y = mx + c and use it to predict the salary for 10 years of experience.

Means x̄ = 5, ȳ = 38. ✓
Σ(x − x̄)(y − ȳ) = (−3)(−8) + (−1)(−4) + (1)(3) + (3)(9) = 24 + 4 + 3 + 27 = 58; Σ(x − x̄)² = 9 + 1 + 1 + 9 = 20. ✓
m = 58 ÷ 20 = 2.9; c = 38 − 2.9 × 5 = 23.5, so y = 2.9x + 23.5. ✓
For x = 10: y = 29 + 23.5 = 52.5 (thousand). ✓

Apply

[3 marks]

For each situation, identify the most suitable category of machine learning: (a) a bank groups transactions to find unusual patterns that may be new types of fraud; (b) a hospital trains a system on X-rays that doctors have already marked as healthy or diseased; (c) a program learns to control the traffic lights at a junction by being scored on how short the queues are.

(a) Unsupervised learning (clustering / anomaly detection, no labels). ✓
(b) Supervised learning (labelled X-rays, classification). ✓
(c) Reinforcement learning (actions scored by a reward, no labelled data). ✓

Analysis

[3 marks]

A teacher fits the line y = 4.1x + 47.7 to data on hours of revision (1 to 5 hours) and test score (out of 100). She uses it to predict the score of a student who revises for 40 hours. Comment on this use of the model.

The prediction is 4.1 × 40 + 47.7 = 211.7, which is impossible for a test out of 100. ✓
40 hours is far outside the range of the training data (1 to 5 hours), so this is extrapolation. ✓
The relationship is unlikely to stay linear, so the model should only be used within (or close to) the range of its training data. ✓

Mark this once you have attempted all six and checked your working. It records a Practiced badge on the topic and adds a one-time bonus. Revealing the solutions alone does not count.