Regression, also known as regression analysis, is also a task that belongs to supervised learning paradigm. We first in this chapter present the regression problem and definition and explain the working principle using formal and illustrated descriptions. Next, we discuss the relevant elements of regression, including the number and relationship of independent variables and dependent variables, linear or nonlinear regressions, parametric or nonparametric regressions, and interpolation or extrapolation of regression fitting lines. We then focus on several commonly used regression algorithms, such as multiple linear regression, polynomial regression, ridge regression, Lasso regression, and Bayesian regression. After that, we explain the loss functions, namely, mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), relative absolute error (RAE), relative squared error (RSE), Huber loss, log-cash loss, and coefficient of determination. Finally, the application fields of regression are introduced briefly.