Learning Paradigms
Learn the major ways models receive supervision or feedback: supervised, unsupervised, semi-supervised, self-supervised, reinforcement and generative learning. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Supervised learningSupervised learning trains on input-output pairs where a target label or numeric outcome is known. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02Unsupervised learningUnsupervised learning discovers structure without an explicit target variable. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03Semi-supervised learningSemi-supervised learning combines a small labelled set with a larger unlabelled set. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Self-supervised learningSelf-supervised learning creates training signals from the data itself, such as masked-token prediction, contrastive pairs or reconstruction. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Reinforcement learningReinforcement learning learns actions through interaction and delayed rewards. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
06Generative learningGenerative learning models the distribution or structure of data so new samples, sequences or representations can be produced. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.