Probabilistic Models for Linear Regression

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 1 of 3

Linear Regression: Statistical Inference and Regularization

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

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Untitled

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Back to Linear Regression

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Regression: Simple and Multiple Linear

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Sparse Regression

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Linear Regression: Estimation and Inference

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

Linear Regression: Basics and Estimation

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

Linear Regression and Logistic Regression

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