Unsupervised learning

As explained in Acing the CCNA Exam: Volume 2 Advanced Networking and Security

Unsupervised learning is a machine-learning approach that discovers patterns or structure in data without predefined labels.

Unsupervised learning trains a machine-learning algorithm on unlabeled data. It identifies underlying patterns and relationships without predefined labels, then organizes related data into clusters. section 22.3.1

Key distinctions:

• Uses unlabeled data, unlike supervised learning, which learns from labeled examples. section 22.3.1
• Finds structure and groups data into clusters, but does not assign human-readable labels automatically. section 22.3.1
• Supports analysis of network data to uncover patterns, anomalies, and insights. section 22.4

Cat-and-dog images

Given many unlabeled cat and dog photos, the algorithm groups images according to detected patterns. A human interprets the clusters and assigns labels such as “cat” or “dog.” section 22.3.1

Figure 22.12 Unsupervised machine learning. Unlabeled data is input into the ML algorithm, allowing it to identify patterns and relationships, categorizing the data into clusters. A human can then assign labels according to their interpretation of the grouped data.

Use the diagram to distinguish unlabeled input, pattern identification, clustering, and subsequent human interpretation. section 22.3.1

How Acing the CCNA Exam: Volume 2 Advanced Networking and Security covers it

How deep it goes

Explains unlabeled training, pattern discovery, clustering, and human interpretation, but does not detail specific algorithms or implementation steps.

Examples

Groups unlabeled cat-and-dog images into clusters before a human assigns interpretations.

What it assumes

Assumes basic machine-learning terminology while distinguishing labeled from unlabeled training data.

What you can do after

Evaluate how unlabeled data can reveal clusters, patterns, anomalies, and other network insights.

How it connects

Contrasts with supervised learning and reinforcement learning, and relates to machine learning, deep learning, and artificial intelligence.

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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 Acing the CCNA Exam: Volume 2 Advanced Networking and Security. The section links point to the parts of the book it draws on.