ml-three-classical-paradigms

IN premise — entries/2026/06/21/wiki-Machine_learning-chunk-3.md

Created 2026-06-21T09:55:50+00:00

The three classical machine learning paradigms are supervised learning (labelled data), unsupervised learning (no labels), and reinforcement learning (reward signal)

Dependents

These beliefs depend on this one:

• OUT llm-pipeline-combines-three-ml-paradigms — The modern LLM training pipeline synthesizes all three classical ML paradigms in sequence: self-supervised pretraining (unsupervised), instruction fine-tuning (supervised), and RLHF alignment (reinforcement learning).
• IN modern-pipelines-dissolve-classical-paradigm-taxonomy — Modern LLM training pipelines dissolve the classical three-paradigm taxonomy — self-supervised pretraining blurs the supervised/unsupervised boundary (its taxonomic status is actively debated), and the full pipeline synthesizes all three paradigms sequentially, suggesting the taxonomy was always a pedagogical convenience rather than a natural partition of learning.