linear_regression_normal_equation.md

Linear regression using the normal equation - numpy implementation

There is a closed-form solution for choosing the best weights and bias for linear regression. The optimal solution achieves the smallest squared error loss.

• Notebook for solving linear regression using normal equation

To understand the mathematics underlying the normal equation, read the following materials.

• Chapter 2 Linear Algebra

• Chapter 4 Numerical Computation, Section 4.3 Gradient-Based Optimization

• Chapter 5 Machine Learning Basics, Subsection 5.1.4 Example: Linear Regression

• Additional materials: proof of convexity of MSE and computation of gradient of MSE