Regression: Linear Models

Related lectures (49)

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Explores Gaussian Mixture Regression in 2D datasets, analyzing priors, components, and regression outcomes.

Covers the concept of multi-linear regression and the least squares method for model fitting.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Explores linear regression fundamentals, emphasizing the importance of regularization techniques to enhance model performance.

Covers the estimation of model coefficients, inference of coefficients, and ANOVA.

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

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