Masinaõppe

Masinaõppe, often translated as machine learning, is a subfield of artificial intelligence that focuses on enabling systems to learn from data without being explicitly programmed. Instead of following a set of predefined instructions, machine learning algorithms identify patterns, make predictions, and improve their performance over time as they are exposed to more information. This learning process typically involves training a model on a dataset, where the model adjusts its internal parameters to minimize errors or optimize a specific objective.

There are several main types of machine learning. Supervised learning involves training models on labeled data, where both the input and the desired output are provided. This is used for tasks like classification and regression. Unsupervised learning, on the other hand, deals with unlabeled data, aiming to discover hidden patterns or structures, such as clustering or dimensionality reduction. Reinforcement learning involves an agent learning through trial and error by interacting with an environment and receiving rewards or penalties.

The applications of machine learning are vast and continually expanding. They are found in everyday technologies such as recommendation systems on streaming services and e-commerce platforms, spam filters in email, and voice assistants. In more specialized fields, machine learning is used for medical diagnosis, financial fraud detection, autonomous driving, and scientific research. The effectiveness of machine learning heavily relies on the quality and quantity of the data used for training, as well as the choice of appropriate algorithms for the specific problem.