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📘 AI systems learn from examples

Many modern artificial intelligence systems learn by showing a computer large numbers of examples and adjusting internal parameters until outputs improve on a task. A spam filter learns from labeled messages, a photo tagger learns from imag

3
lessons
~15 min
to learn
Adults
level
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What you’ll learn

  1. Patterns in dataExplain how labeled examples and features enable pattern learning from data.AI systems map inputs to outputs by finding statistical patterns in training examples.
  2. Training and modelsDescribe training, architecture choice, and the shift to inference in deployment.Weights adjust on labeled data; architectures match task type; serving runs the fixed model.
  3. Limits and useIdentify overconfidence, data governance, and practical checks before trusting outputs.Models can err fluently; privacy and bias matter; define tasks and controls before deployment.

Questions this course answers

What do labels provide in supervised learning?

Labels define what success means for each training example.

Why keep a separate test set?

Held-out data reveals whether the model generalizes or merely memorizes.

What is a common deployment risk?

Probabilistic systems can be wrong while sounding plausible.

Grounded in trusted sources

  • National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework
  • National Institute of Standards and Technology, AI glossary, https://www.nist.gov/itl/ai-risk-management-framework/glossary
  • U.S. Government Accountability Office, Artificial Intelligence: An Accountability Framework for Federal Agencies, https://www.gao.gov/products/gao-21-519sp
  • European Union, AI Act overview, https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

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