Which procedure do computers use to learn patterns from data?

Computers utilize machine learning algorithms to learn patterns from data by analyzing and interpreting large datasets. Machine learning algorithms are designed to identify relationships and structures in the data without being explicitly programmed for each specific task. These algorithms can adapt and improve their performance over time as they are exposed to more data, allowing them to make predictions or decisions based on what they have learned.

Machine learning encompasses various techniques, such as supervised learning, unsupervised learning, and reinforcement learning, each of which approaches the pattern recognition in unique ways. For instance, supervised learning uses labeled data to train a model, while unsupervised learning finds hidden patterns in unlabeled data.

Other options, while related to data handling and analysis, do not specifically refer to the process of computers learning from data. Data mining algorithms often involve discovering patterns but do not focus on learning over time in the same way. Statistical algorithms involve techniques for analyzing data but do not emphasize machine learning concepts. Predictive algorithms are a subset of machine learning focused on forecasting outcomes, but they do not encompass the broader learning-from-data process that machine learning algorithms cover.