Lectures related to Cubic Splines and Least-Squares Approximation

Explains spline interpolation and error analysis in approximating data points using piecewise linear and spline methods.

Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Explores Lagrange interpolation, emphasizing uniqueness and simplicity in reconstructing functions from limited values.

Explores interpolation of regular functions, error analysis, convergence, and Chebyshev polynomials.

Covers polynomial interpolation, error analysis, stability, and piecewise linear interpolation using equally distributed nodes.

Covers the optimization of error in polynomial interpolation, focusing on minimizing the error by strategically placing interpolation points.

Covers the Newton method for finding zeros of functions using data interpolation.