Fundamentals of Neural Networks - Linear Regression

The video tutorial introduces statistical machine learning, focusing on linear regression. It explains the linear regression model, its components, and the ordinary least squares (OLS) method for optimization. The tutorial discusses the importance of continuous Y values and the challenges posed by discrete Y values, leading to an introduction to logistic regression for classification tasks.

What is the difference between features, covariates, and variables in the context of linear regression?

Describe the relationship between the mean square error and the optimization of weights in linear regression.

What challenges arise when the target variable Y is discrete in a linear regression model?