Readings

Lectures

Develop your answers to the following guiding questions while completing the readings and as you work through the module.

What is a linear regression problem?
What are the explanatory variables and dependent variable in a linear regression problem?
How is a linear regression task different from a classification problem?
How many equations do we have for a linear regression problem? How many unknown do we need to solve for? How do we deal with this?
What’s the least square method? What is it trying to minimize?
What’s the canonical solution of least square method in matrix form?
How do we train and predict with a linear regression model?
How do we evaluate a linear regression model?
How do we deal with non-linear relationship using linear regression model?
What if we have outliers in the data, how do we deal with it when modeling with linear regression?
What is the regularized linear regression method (Ridge regression)?
What is the nearest neighbor regression? What the pros and cons of this method?