Artificial intelligence · self-study course
AI foundations and responsible use
Understand learning tasks, datasets, honest evaluation, privacy and human responsibility. No paid AI tool is required.
Your learning path
- 01
AI, machine learning and useful problem definitions
Artificial intelligence is a broad field of methods for tasks involving perception, prediction, planning or language. Machine learning fits patterns from data rather than requiring every rule to be handwritten.
Grades 9–12 · Notes · 8 questions - 02
Examples, features, labels and data quality
An example is one unit of observation. Features are inputs available at prediction time; a label is the target used for supervised learning.
Grades 9–12 · Notes · 8 questions - 03
Training, validation and honest testing
The model fits its parameters on training data. Success there does not alone demonstrate generalization to new cases.
Grades 9–12 · Notes · 8 questions - 04
Responsible AI, fairness and verification
Describe who uses the system, who may be affected, and who can correct mistakes. Automation does not remove human responsibility.
Grades 9–12 · Notes · 8 questions