Machine Learning (ML)
A subset of AI in which a system improves at a task by finding statistical patterns in data, rather than following only rules a person wrote explicitly in advance. Most current AI policy frameworks target this layer specifically, because a learning system's behavior is harder to fully predict in advance than a fixed-rule system's.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in The AI Lobbyist: Certified AI Policy Strategist.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A subset of AI in which a system improves at a task by finding statistical patterns in data, rather than following only rules a person wrote explicitly in advance. Most current AI policy frameworks target this layer specifically, because a learning system's behavior is harder to fully predict in advance than a fixed-rule system's.
AI that discovers patterns in data to make predictions or classifications without being explicitly programmed for each case.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- What is AI? Definitions, History, and Types · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- What AI Really Is: No Hype, No Myths, No Fear · Foundations of AI for Lobbyists, The AI Lobbyist: Certified AI Policy Strategist
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.