NSWSoftware EngineeringSyllabus dot point

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

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.

Generated by Claude OpusReviewed by Better Tuition Academy5 min answerUpdated 2026-05-20

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What this dot point is asking

NESA wants you to know the major categories of industrial ML deployment, the type of learning each uses, and a realistic deployment challenge. The four big categories: image recognition, natural language processing, recommendations, predictive maintenance.

The answer

Image recognition

Computer vision systems classify or detect objects in images. Applications:

Medical imaging: detecting pneumonia in chest x-rays, cancer in pathology slides, diabetic retinopathy in retinal scans.
Autonomous driving: detecting pedestrians, vehicles, traffic signs, road markings.
Quality control: identifying defects on a factory production line.
Agriculture: identifying weeds or pest damage from drone imagery.
Retail: cashier-less stores (Amazon Go) tracking which items shoppers take.

Learning type: supervised classification or object detection. Typically convolutional neural networks (CNNs).

Challenges: requires very large labelled datasets, must work across lighting and equipment variations, ethical issues around surveillance.

Natural language processing (NLP)

Systems that understand or generate human language. Applications:

Machine translation (Google Translate).
Sentiment analysis of customer reviews or social media.
Question answering and chatbots.
Summarisation of long documents.
Code generation (GitHub Copilot, Claude Code).
Email and document search.

Learning type: supervised pre-training plus task-specific fine-tuning. Modern systems use transformer architectures, especially large language models.

Challenges: large compute cost for training and inference, hallucination (confident wrong answers), cultural and language coverage gaps, prompt injection attacks.

Recommendation systems

Predict items a user is likely to want. Applications:

Netflix, YouTube, Spotify: what to watch or listen to next.
Amazon, eBay: products a customer is likely to buy.
News feeds (Facebook, X, TikTok): which posts to show.
Job sites (LinkedIn, Seek): roles matched to a candidate.

Learning type: a mix of collaborative filtering (find users similar to you, recommend what they liked), content-based filtering (find items similar to ones you liked), and reinforcement learning to optimise long-term engagement.

Challenges: cold start (new users or items have no history), filter bubbles (showing only what users already agree with), measuring success (clicks vs satisfaction vs long-term wellbeing).

Predictive maintenance

Predict when industrial equipment will fail. Applications:

Manufacturing: motors, pumps and bearings in factories.
Energy: wind turbines, transformers, power lines.
Transport: aircraft engines, train wheels, ship engines.
Mining: haul truck components.

Learning type: supervised regression (time to failure) or classification (will it fail in the next N days), with anomaly detection as a complement.

Challenges: rare-event labels (most machines do not fail in any given week), cost-sensitive evaluation (false negatives cost more than false positives), sensor noise and missing data.

Other categories worth knowing

Fraud detection: classifying transactions as legitimate or fraudulent.
Demand forecasting: predicting retail or energy demand.
Translation and accessibility: real-time captions, sign-language recognition.
Drug discovery: predicting which molecules bind to a target.
Generative AI: image, video, audio and text generation.

A worked Python example

A simple sentiment analysis pipeline using scikit-learn:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

reviews = [
    "The food was amazing and the service was great.",
    "Terrible experience, will not return.",
    "Excellent, I loved every dish.",
    "Boring and overpriced.",
]
labels = [1, 0, 1, 0]  # 1 = positive, 0 = negative

pipeline = Pipeline([
    ("tfidf", TfidfVectorizer()),
    ("logreg", LogisticRegression()),
])
pipeline.fit(reviews, labels)

print(pipeline.predict(["The meal was fantastic"]))  # [1]
print(pipeline.predict(["I hated the waiter"]))      # [0]

This is a tiny example, but the structure generalises: convert text to numbers, train a classifier, predict on new text.

Australian context

CSIRO runs ML projects in agriculture (weed detection from drones) and climate modelling.
Cochlear uses ML in hearing implant signal processing.
Big four banks use ML for fraud detection, credit scoring and customer service routing.
Atlassian, Canva ship ML features in their products (smart search, content generation).
Telstra and major mining companies use predictive maintenance on infrastructure and equipment.

Deployment realities

Deploying ML is more than training a model:

Data pipelines keep training data fresh.
Model serving runs the trained model at inference time, often at scale.
Monitoring detects when the model's predictions drift from reality.
Retraining updates the model when data shifts.
A/B testing compares model versions against a baseline.
Fallbacks provide a safe response when the model is uncertain.

This is sometimes called MLOps.

Worked example

A bank wants to use ML to detect fraudulent credit card transactions. Outline the design.

Data: every transaction with features (amount, merchant, time, location, distance from last transaction). Labels: confirmed fraud from chargebacks.
Learning type: supervised classification. Severe class imbalance (99.9 percent legitimate), so use techniques like class weighting or specialised loss.
Deployment: the model scores every incoming transaction in real time. Above a threshold, the transaction is held for review. Below it, it goes through.
Monitoring: track precision, recall, false positive rate, and the rate of customer complaints. Retrain monthly.
Fallback: if the model service is unavailable, fall back to simple rules ("decline transactions over $10,000 from a new device") rather than going down.

Challenges: fraudsters adapt their tactics, so the model must be retrained constantly. False positives (declining legitimate transactions) hurt customer trust, so threshold tuning is critical.

Common traps

Listing only one type of learning: Industry usually combines several. Recommendation systems use collaborative filtering plus content-based filtering plus RL.
Forgetting the deployment layer: Training a model is the easy bit. Serving it reliably, monitoring it and retraining it are the hard parts. Markers reward mentioning MLOps activities.
Treating image recognition as a solved problem: Domain shift (different cameras, lighting, populations) breaks deployed models. Continuous evaluation matters.
Conflating "ML system" with "deep learning": Many industrial deployments still use logistic regression, gradient-boosted trees or random forests. Deep learning is one tool, not the only one.
Ignoring cost asymmetry: In fraud, missing a fraud costs more than declining a real customer; in cancer screening, missing a tumour costs more than a false alarm. Evaluation must reflect costs, not just accuracy.

Past exam questions, worked

Real questions from past NESA papers on this dot point, with our answer explainer.

2024 HSC5 marksDescribe two industry applications of machine learning. For each, identify the type of learning used and one challenge in deploying the system.

Show worked answer →

Application 1: medical image analysis.

Used by hospitals to assist radiologists in reading chest x-rays for pneumonia, mammograms for breast cancer, and dermatology images for skin cancer. The model classifies an image as normal or suggesting a particular condition.

Type of learning: supervised classification, typically a convolutional neural network trained on thousands of images each labelled by a radiologist.

Deployment challenge: regulatory approval. Medical AI is classed as a medical device by health regulators (TGA in Australia, FDA in the US) and must pass clinical evaluation. Models also need to be robust across imaging equipment from different manufacturers and patient populations, which requires diverse training data.

Application 2: predictive maintenance in industrial plants.

Used by manufacturers and energy companies to predict when machinery will fail. Sensors on motors, pumps and turbines stream vibration, temperature and acoustic data. A model predicts which machines will fail within the next two weeks so they can be serviced proactively.

Type of learning: supervised regression or classification, sometimes with anomaly detection on top. Training data is sensor readings paired with historical failure dates.

Deployment challenge: rare-event problem. Failures are infrequent, so training data is imbalanced. The model must avoid both false negatives (missed failures that lead to expensive downtime) and false positives (unnecessary maintenance). Cost-sensitive evaluation is essential.

Markers reward two distinct applications, the correct type of learning for each, a specific deployment challenge (not just "it is hard"), and ideally one mitigation per challenge.