Parameters

The internal numbers a model adjusts during training.

The quantities $\theta$ a learning algorithm actually solves for, chosen to fit the training data as well as the model's objective allows -- distinct from a hyperparameter, which is set before training begins and never adjusted by the fitting procedure itself.

Examples

A linear regression's coefficients and intercept are its parameters.
A neural network's weights and biases are its parameters.