← Software Engineering syllabus
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.
5 min answer → - Describe applications of machine learning in industry, including image recognition, natural language processing, recommendation systems and predictive maintenance
A focused answer to the HSC Software Engineering Module 3 dot point on ML applications. Image recognition, NLP, recommendations, predictive maintenance, the worked example, and the traps markers look for.
5 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, the worked example, and the traps markers look for.
5 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.
5 min answer → - Describe the basic structure of a neural network, including neurons, layers, weights, activation functions and training by backpropagation
A focused answer to the HSC Software Engineering Module 3 dot point on neural networks. Neurons, layers, weights, activation functions, forward pass, backpropagation, the worked example, and the traps markers look for.
6 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, the worked example, and the traps markers look for.
5 min answer →