How deeply does The AI Pocket Book cover unsupervised learning?

Explains clustering and evaluation ambiguity, but stops short of specifying an algorithm or formal success measure.

Unsupervised learning

A machine learning approach that seeks patterns in unlabeled data without a target output.

Unsupervised learning finds patterns in data without examples of a correct output. It explores what is already there rather than training against a single target answer. section 1.27

Grouping patients

A system can group patients by their medical records into similar clusters. Two, three, or five groups might all be defensible because no uniquely correct grouping exists. section 1.27

The practical catch is evaluation:

Clustering is the common example: similar data points are grouped together. section 1.27
Success is not clear-cut because there is no uniquely right model or group assignment. section 1.27
Rules of thumb such as choosing the curve’s “knee” or “elbow” can be poorly defined. section 1.27

How The AI Pocket Book covers it

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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 The AI Pocket Book. The section links point to the parts of the book it draws on.