Lectures related to Curve fitting

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Covers the topology of Riemann surfaces, focusing on orientation and orientability.

Covers SciPy constants, curve fitting, and zero finding methods for scientific computing.

Covers advanced Matlab programming topics such as cell arrays, 3D surfaces, and interpolation.

Covers the inference steps for max-stable processes and the fitting of extreme rainfall models.

Covers the importance of calibration curves in ensuring accurate sample quantitation in ligand binding assays.

Covers the equations for curves and surfaces in space, including parametric and particular surfaces.

Explores optimal transport, gradient flows, the implicit Euler scheme, and the heat equation in the context of Dirichlet energy functional.

Presents a proof that smooth projective varieties with nef anti-canonical divisor have surjective Albanese morphism.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Explores logistic and Dirichlet densities, parametric models, and asymmetric alternatives.

Covers the basics of Bezier curves and introduces Bernstein polynomials.

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Explores curve fitting via polynomial interpolation, stability, errors, and node distribution impact.

Explains least squares approximation for finding best fit lines or curves to data points.

Introduces nonlinear equations and polynomial interpolation for curve fitting.

Explores polynomial interpolation for curve fitting, error analysis, and the Runge's phenomenon.

Explores parameter estimation of SDEs using Linear Response Theory and covers challenges, examples, algorithms, and convergence.

Explores fundamental groups, homotopy classes, and coverings in connected manifolds.

Explores signal sampling theory, interpolation techniques, and the importance of the sampling theorem in signal processing.