Gaussian Conditional Model and Properties

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Linear Regression: Statistical Inference Perspective

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

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: Statistical Inference and Regularization

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

Probabilistic Linear Regression

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

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression Basics

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

Linear Regression Basics

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

Supervised Learning: Linear Regression

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

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.