Singular Value Decomposition: Theory and Applications

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

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Least Squares Solutions

Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.

Singular Value Decomposition: Applications and Solutions

Explores Singular Value Decomposition, matrix solutions, and least squares regression in data analysis.

Linear Models: Ridge, OLS and LASSO

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.

Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

QR Factorization: Least Squares System Resolution

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Least Squares Approximation

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