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?
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.
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.
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.
Inquiry Question 1: How do machine learning systems work?
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.
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.
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.
