Lectures related to Newton's method: Hessian and optimization

Matrix Computations: Complexity and Solvers

Explores the complexity of matrix computations, focusing on solving least squares problems and the impact of noise on numerical stability.

Multilinear Regression: Basics and Applications

Covers the basics of multilinear regression and its application in analyzing material properties.

Heteroskedasticity and Autocorrelation

Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.

Hadamard Design: Introduction and Construction

Introduces Hadamard and Plackett-Burman designs, explaining construction, analysis, and optimization techniques.

Least Squares Solutions

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

Linear Regression: Regularization

Covers linear regression, regularization, and probabilistic models in generating labels.