Which type of machine learning algorithm learns from outcomes to make decisions?

The type of machine learning algorithm that learns from outcomes to make decisions is reinforcement learning. In reinforcement learning, an agent interacts with an environment and learns to make decisions based on the rewards or penalties it receives as a consequence of its actions. The main focus is on determining actions that maximize the cumulative reward, which means that the algorithm learns from the outcomes of its previous actions and adjusts its behavior accordingly.

This learning paradigm is particularly important for applications where the environment is dynamic and the consequences of actions are not immediately clear. By continuously exploring different actions and receiving feedback in terms of rewards, the agent builds a policy that helps it make better decisions over time.

In contrast, supervised learning involves learning from labeled data with known outcomes either to predict future outcomes or classify data, while unsupervised learning focuses on finding patterns or groupings in unlabeled data without any explicit outcomes. Deep learning is a subset of machine learning that uses multi-layered neural networks and can operate in both supervised and unsupervised contexts, but it does not inherently center around learning through outcomes as reinforcement learning does.