Model Building: Linear Regression

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Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

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

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Covers the estimation of optimism for predictors via statistical models and the application of AIC to linear regression.

Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.

Introduces linear regression analysis, covering model building, predictors, coefficients, and outcome interpretation.

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

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

Explores statistical estimation, regression models, and model selection in parametric models.

Explores linear models, least squares, Gaussian vectors, and model selection methods.