Lectures related to Linear Regression

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Linear Regression: Least Squares

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Linear Regression: Maximum Likelihood Approach

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Linear Regression: Basics and Applications

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

Linear Regression: Basics and Applications

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Nonparametric Statistics: Bayesian Approach

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Linear Regression: Basics and Interpretation

Introduces the basics of linear regression, interpreting coefficients, assumptions, transformations, and 'Difference in Differences' for causal analysis.

Linear Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Linear Regression: Foundations and Applications Log in to Mediaspace to watch this video

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

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

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

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

Linear and Weighted Regression: Optimal Parameters and Local Solutions Log in to Mediaspace to watch this video

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.