What are the methods of machine learning?

Machine learning methods mainly include the following:

Supervised learning: Supervised learning is one of the most commonly used methods in machine learning. In supervised learning, the system will be given a set of sample data with known input and output, and the system needs to learn a function so that the function can predict the correct output according to the given input.

Unsupervised learning: Unsupervised learning is another common method in machine learning. In unsupervised learning, the system only has input data and no output data. The system needs to learn a function that can automatically classify the input data.

Semi-supervised learning: Semi-supervised learning is a method of mixed supervised learning and unsupervised learning. In semi-supervised learning, the system will be given some known input and output sample data and some unknown input data. The system needs to learn a function by using known sample data, so that the function can predict the correct output according to unknown input data.

Reinforcement learning: Reinforcement learning is a learning method based on environment and feedback, and the system learns the optimal strategy through constant interaction.

Clustering: Clustering is an unsupervised learning method in machine learning. Its purpose is to divide data into different groups, so as to maximize the data similarity within groups and minimize the data similarity between groups.

Dimension reduction: Dimension reduction is an unsupervised learning method in machine learning. Its purpose is to reduce the dimension of data and make it easier to analyze.

Deep learning: Deep learning is a learning method based on neural network in machine learning, which simulates human brain to learn by constructing multi-layer neural network. Deep learning is widely used in computer vision, speech recognition, natural language processing and other fields.

Recurrent neural network: Recurrent neural network is a special deep learning method, which recursively processes sequence data and is widely used in natural language processing, speech recognition and other fields.

Bayesian learning: Bayesian learning is a learning method based on probability theory and statistics, learning and forecasting through Bayesian theorem.

Statistical learning method: Statistical learning method is a machine learning method based on statistical theory, which learns and predicts through statistical model and optimization algorithm. Include linear regression, logistic regression, naive Bayes, etc.

These methods have their own characteristics and scope of application. In practical application, we should choose the appropriate method according to the specific situation of the problem.