Mathematics plays a vital role in machine learning. First of all, the core of machine learning is algorithm, and the design and implementation of algorithm can not be separated from the support of mathematics. For example, linear regression, logistic regression, support vector machine and other commonly used machine learning algorithms are all derived from mathematical theory.

Secondly, data processing and feature engineering in machine learning are also inseparable from mathematical methods. For example, operations such as normalization, standardization and denoising in data preprocessing require mathematical knowledge; Feature selection and dimension reduction technology are also based on mathematical principles.

In addition, the optimization problem in machine learning also needs mathematical methods. For example, the gradient descent method is an optimization algorithm based on calculus, which can help us find the optimal solution.

Finally, the model evaluation and verification in machine learning also need mathematical methods. For example, cross-validation is a commonly used model evaluation method, which requires statistical knowledge to calculate the performance index of the model.

In a word, mathematics has many functions in machine learning, providing us with theoretical basis and tool support to help us better understand and apply machine learning technology.