Learning Paradigms of Machine Learning Algorithms

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Learning paradigm defines how the algorithm processes and learns from training data, whether through supervision, exploration, or pattern discovery, which determines its applicability to different problem types

Reinforcement Learning: Reinforcement learning algorithms learn optimal behaviors through trial-and-error interactions with environments, maximizing cumulative rewards over time.
Reinforcement Learning:
-: Algorithms with unspecified learning paradigms may combine multiple approaches or represent novel methodologies not fitting traditional categories.
Self-Supervised Learning: Algorithms that learn representations from unlabeled data by creating supervisory signals from the data itself.
Transfer Learning: Algorithms that apply knowledge gained from one domain to improve performance in related but different domains.
Semi-Supervised Learning: Algorithms that leverage both labeled and unlabeled data to improve learning performance beyond supervised methods.

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