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Module 3: Software Automation
Quick questions on Supervised, unsupervised and reinforcement learning explained: HSC Software Engineering Module 3
9short 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
The training data has both features and labels. The algorithm learns the function from features to label. Two sub-categories:
What is unsupervised learning?Show answer
The training data has features but no labels. The algorithm finds structure in the data on its own. Sub-categories:
What is reinforcement learning?Show answer
An agent learns by interacting with an environment, receiving rewards or penalties for its actions. The algorithm learns a policy that maximises long-term reward.
What is a worked Python example?Show answer
data = load_breast_cancer() X = data.data # 30 features per tumour y = data.target # 0 = malignant, 1 = benign
What is a worked example?Show answer
X, _ = make_blobs(n_samples=300, centers=4, random_state=42)
What is how to choose?Show answer
Most real systems combine paradigms. A recommendation engine might use unsupervised clustering to discover taste groups, supervised regression to predict ratings, and reinforcement learning to optimise long-term engagement.
What is confusing reinforcement learning with supervised learning by example?Show answer
RL learns from rewards over time. Supervised learning learns from immediate correct answers.
What is treating unsupervised learning as easier because there are no labels?Show answer
It is often harder: there is no objective right answer to compare against, so evaluating model quality is qualitative.
What is forgetting semi-supervised and self-supervised learning?Show answer
Real-world problems often fall in between. Large language models use self-supervised pretraining (predict the next word) on unlabelled text. This is not in scope for HSC but worth knowing.