Here is a linear regression model
$$y = mx + b,$$
where $b$ is known as $y$-intercept, but also known as the bias , $m$ is the slope, and $x$ is the feature vector.
As I understood, in machine learning, there is also the bias that can cause the model to underfit.
So, is there a connection between the bias term $b$ in a linear regression model and the bias that can lead to under-fitting in machine learning?