Machine Learning Paradigms: Beyond Rigid Categories

While traditional taxonomies (supervised, unsupervised, etc.) provide a useful starting point, real-world problems often blend techniques. These categories are tools that are combined to create bespoke solutions.

For example: Semi-Supervised Learning mixes a small amount of labeled data with a large unlabeled dataset; Self-Supervised Learning generates labels from data structure; Reinforcement Learning combined with Imitation Learning leverages expert demonstrations; Transfer Learning plus Online Learning adapts pre-trained models continuously; and Unsupervised Clustering with Supervised Finetuning reduces labeling effort while maintaining insights.