Final Answer:
The type of machine learning algorithm often described as learning by trial and error is Reinforcement Learning.
Step-by-step explanation:
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or punishments based on the actions it takes, and its objective is to learn a strategy that maximizes cumulative rewards over time. This process is analogous to trial and error, as the agent explores different actions, receives feedback, and adjusts its strategy accordingly. Notable applications of reinforcement learning include game-playing algorithms, robotics, and autonomous systems where the algorithm learns optimal behavior through continuous interaction with the environment.