Supervised learning

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

Supervised learning is a machine-learning approach that trains an algorithm on examples paired with known labels.

Supervised learning trains a machine-learning algorithm with labeled data: each input example has a corresponding known label. The algorithm learns relationships between inputs and labels rather than relying on explicitly programmed rules. section 22.3.1

Distinguish supervised learning from related approaches:

• Uses labeled examples, unlike unsupervised learning, which finds patterns in unlabeled data. section 22.3.1
• Differs from reinforcement learning, which learns through interaction with an environment and positive or negative feedback. section 22.3.1
• Supports classification by learning to associate inputs with known categories. section 22.3.1

Cat-and-dog recognition

Train the algorithm with thousands of cat and dog photographs labeled accordingly. With enough examples, it learns to distinguish cats from dogs in new images. section 22.3.1

Figure 22.11 Supervised machine learning. Labeled data is input into the ML algorithm, allowing it to learn the relationships between the data and the given label.

Shows labeled data entering the algorithm and producing learned relationships between the data and its labels. section 22.3.1

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

How deep it goes

Defines labeled training and contrasts learning approaches, but stops short of algorithmic implementation or evaluation procedures.

Examples

Uses thousands of labeled cat and dog photographs to demonstrate image classification.

What it assumes

Assumes basic awareness of artificial intelligence and machine learning while defining labeled data from the ground up.

What you can do after

Evaluate/understand how labeled examples enable algorithms to distinguish categories.

How it connects

Connects supervised learning with machine learning, unsupervised learning, reinforcement learning, semi-supervised learning, and deep learning.

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Related concepts

• 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.
• unsupervised learningcontrasts with · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• labeled datauses · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
• reinforcement learning with human feedbackused by · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
• classifierincludes · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• linear regressionincludes · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

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.