Unsupervised learning

As explained in Software Testing with Generative AI

A machine-learning approach that trains a model on unlabeled data to discover implicit patterns or structure.

Unsupervised learning trains an algorithm on unlabeled data without paired outputs, allowing a model to learn implicit patterns and structure. section 2.1

Training processes large datasets into model weights that support prediction; unlike supervised learning, the data is not organized around labeled outcomes. section 2.1 section 2.1

Keyboard prediction

A phone keyboard assigns probabilities to possible words after “I am,” favoring “away” over unlikely alternatives such as “sandalwood.” section 2.1

Figure 2.1 Probability distribution in action

Visualizes how a probability distribution ranks possible next words. section 2.1

How Software Testing with Generative AI covers it

How deep it goes

Defines unlabeled-data training and implicit pattern learning, then sketches datasets, algorithms, probabilities, and model weights.

Examples

Uses keyboard next-word prediction and contrasts it with labeled medical data paired with illness outcomes.

What it assumes

Distinguishes labeled inputs and paired outcomes from unlabeled data without requiring prior algorithmic detail.

What you can do after

Evaluate/understand how unlabeled training produces predictive probabilities and implicit patterns.

How it connects

Contrasts supervised learning and connects probability distributions, predictive models, and language modeling.

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Related concepts

• supervised learningcontrasts with · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• K-means clusteringincludes · K-means clustering partitions data into a specified number of groups by assigning observations to representative centers and iteratively updating those centers to reduce within-group distances.
• density estimatorincludes · A density estimator is a statistical method or model that estimates the probability density underlying observed data.
• hierarchical clusteringincludes · Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
• machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• clusteringincludes · Clustering is the technique of grouping data items according to similarity or shared characteristics.

This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.