# Category:Mathematics: Machine Learning

Machine learning is a branch of artificial intelligence that deals with the study of algorithms and statistical models that enable computers to learn from data and improve their performance without being explicitly programmed. Machine learning is a mathematical field, and it involves the use of probability theory, statistics, optimization, and linear algebra.

There are several types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In supervised learning, a model is trained on a labeled dataset, which means that the correct output for each input is provided. The model then makes predictions on new, unseen data. In unsupervised learning, the model is not provided with labeled data, and it must find patterns and structure in the data on its own. Semi-supervised learning is a mix of supervised and unsupervised learning, where the model is provided with a small amount of labeled data and a large amount of unlabeled data. Reinforcement learning is a type of machine learning that deals with learning from a scalar feedback signal, called a reward.

Machine learning has many applications in a wide range of fields, including computer vision, natural language processing, speech recognition, and drug discovery. It is also used in the field of finance, healthcare, manufacturing and transportation.

The Mathematics used in Machine learning includes Linear Algebra, Calculus, Probability and Statistics, Optimization, Information theory and Numerical Analysis.

## Pages in category "Mathematics: Machine Learning"

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