Lectures related to Spline Interpolation and Approximation

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Covers the basics of Newton interpolation and interpolation polynomials using Lagrange and Newton methods.

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Explains matrix operations, orthogonality, and least squares method in solving linear equations.

Covers the analysis of various data attributes and linear regression models.