Lectures related to Hermite interpolation

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

Explores trigonometric interpolation for approximating periodic functions and signals using equally spaced nodes.

Introduces Lagrange interpolation for approximating data points with polynomials, discussing challenges and techniques for accurate interpolation.

Explores Bayes risk, generalization, error rates, and interpolation methods in machine learning.

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

Explores Lagrange interpolation for polynomial construction and introduces the definite integral.

Introduces nonlinear equations and polynomial interpolation for curve fitting.

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Covers Lagrange interpolation as a unique polynomial of degree 2N through 2N + 1 points.

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Explores splines, emphasizing the least-squares method for interpolating splines and demonstrating its application using MATLAB.

Covers the Lagrange polynomial interpolation method and error analysis in function approximation.

Covers delay compensation in communication channels, including estimation and implementation.

Explores finite dimensional spaces, covering extraction process, bases generation, and space completion.

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

Explores the interpolation of a continuous function by a polynomial.

Explores the inefficiencies of the incorrect method for polynomial interpolation.

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

Covers polynomial interpolation and least-squares approximations to minimize errors.