Ai Learning Paradigms

AI learning paradigms refer to the various approaches and methodologies used to train artificial intelligence systems. These paradigms include supervised learning, where models learn from labeled data; unsupervised learning, which focuses on identifying patterns in unlabeled data; and reinforcement learning, where agents learn to make decisions through trial and error in an environment. Each paradigm offers distinct strategies for enabling machines to improve their performance and adapt to new information.