masinõpetus

Masinõpetus, often translated as machine learning, is a subfield of artificial intelligence that enables computer systems to learn from data without being explicitly programmed. Instead of following a fixed set of instructions, machine learning algorithms identify patterns and make predictions or decisions based on the data they are trained on. This learning process typically involves analyzing large datasets, identifying correlations, and building models that can generalize to new, unseen data.

There are several major categories of machine learning. Supervised learning involves training a model on labeled data, where each input example is paired with a correct output. The model learns to map inputs to outputs. Unsupervised learning, on the other hand, deals with unlabeled data, aiming to find hidden structures or patterns within the data, such as clustering similar data points together. Reinforcement learning focuses on training agents to make a sequence of decisions in an environment to maximize a cumulative reward. The agent learns through trial and error.

Machine learning has a wide range of applications across various industries. It powers recommendation systems on streaming services and e-commerce platforms, enables spam detection in emails, facilitates image and speech recognition, and is crucial for developing autonomous vehicles. As data availability continues to grow, machine learning is expected to play an even more significant role in shaping technological advancements and solving complex problems.