Module 3: Software Automation

6 dot points across 2 inquiry questions. Click any dot point for a focused answer with worked past exam questions where available.

Inquiry Question 2: How are machine learning systems used to develop solutions?

Identify the ethical implications of automation and artificial intelligence, including accountability, transparency, employment effects and the use of personal data

A focused answer to the HSC Software Engineering Module 3 dot point on AI ethics. Accountability, transparency, employment, personal data, real cases (COMPAS, Amazon hiring, Robodebt), the worked example, and the traps markers look for.

9 min answer →

Describe applications of machine learning in industry, including image recognition, natural language processing, recommendation systems and predictive maintenance

A full study guide to the HSC Software Engineering Module 3 dot point on ML applications: image recognition, NLP, recommendation systems, predictive maintenance, an owned MLOps lifecycle figure, worked examples, common traps, and graded practice.

9 min answer →

Explain how the quality and representativeness of training data affect a model, including the risks of bias and overfitting

A focused answer to the HSC Software Engineering Module 3 dot point on training data. Sample bias, label bias, the train/test split, overfitting and underfitting, worked examples, and the traps markers look for.

10 min answer →

Inquiry Question 1: How do machine learning systems work?

Distinguish machine learning from classical programming, and define the roles of model, features, training data and predictions

A focused answer to the HSC Software Engineering Module 3 dot point on what machine learning is. Classical programming vs ML, the role of training data, features, model and predictions, the worked example, and the traps markers look for.

9 min answer →

Describe the basic structure of a neural network, including neurons, layers, weights, activation functions and training by backpropagation

A full study guide to the HSC Software Engineering Module 3 dot point on neural networks: neurons, layers, weights, activation functions, an owned network figure, forward pass and backpropagation, worked numeric examples and graded practice.

10 min answer →

Compare supervised, unsupervised and reinforcement learning, and identify a typical application of each

A focused answer to the HSC Software Engineering Module 3 dot point on learning paradigms. Supervised classification and regression, unsupervised clustering, reinforcement learning, applications of each, worked examples, and the traps markers look for.

10 min answer →