Lectures related to Model with Interactions

Explores Fermat's Theorem, factorization of integers, properties of Z[i], and Hurwitz quaternions.

Ordinary Least Squares Regression Analysis

Covers Ordinary Least Squares Regression analysis and regression model restrictions.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Linear Regression: Concepts and Applications

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Bias-Variance Tradeoff in Ridge Estimation

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.