Geometry of the Lasso

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By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.

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

Explores non-smooth minimization, compressive sensing, sparse signal recovery, and simple representations using atomic sets and atoms.

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

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

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Introduces regularization techniques to prevent overfitting in machine learning models.

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

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

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

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