Linear Regression: Fundamentals and Goodness of Fit

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

Covers the basics of polynomial regression and regularization to prevent overfitting.

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

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

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

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

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

Explores linear regression analysis of ozone data using statistical models.

Covers the analysis of various data attributes and linear regression models.