Lecture 26 #

1. Solving least-square linear regression problem
2. General idea of basis vector in general linear regression problem
3. Types of basis vector (polynomial, Fourier, etc.)
4. Deriving $Ax = b$ problem from least square method for general linear regression curve-fitting
5. Linear regression with multiple input variables
6. Nonlinear regression introduction

Links #

1. Lecture notes