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Supervised learning: The Founder's Guide to AI Fundamentals

Picture of Hoala Greevy Hoala Greevy July 24, 2025

From the founder Definitions

Supervised learning: The Founder's Guide to AI Fundamentals

Supervised learning is a type of machine learning where the model is trained on a labeled dataset, meaning each training example is paired with an output label.

In other words, supervised learning teaches a machine by example:

You give it inputs and the correct answers, so it learns to predict the answer for new, unseen inputs.

Common types of supervised learning

As it relates to email security, a common type of supervised learning is spam detection.

For example:

Goal: Classify incoming email as either spam or ham (not spam)
Input: Emails labeled spam or ham
Output: For each new email passing through the supervised learning algorithm, label it spam or ham

How does it differ from unsupervised learning?

Unsupervised learning involves training a model on unlabeled data.

In other words, the machine learning algorithm tries to find patterns or structures in data without any labels.

For example:

Goal: Segment a customer base based on their behaviors
Input: Customer data
Output: Groups of similar customers

How does supervised learning differ from reinforcement learning?

A core principle of reinforcement learning is that it interacts with an environment and gathers feedback via rewards or penalties.

For example:

Goal: Learn how to play chess
Input: At every step, the algorithm receives input from the environment in the form of a state. The state captures the current situation of the environment. In this example, the state is the layout of the chess board.
Output: The algorithm then produces an output—an action taken based on the current state. For each action’s outcome, a reward signal is generated, which can be either positive or negative (i.e., a penalty)