Geometry of the Lasso

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Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

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

Covers stochastic optimization, sparsity, and non-smooth minimization via subgradient descent.

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

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

Explores concise signal models, compressive sensing, sparsity, atomic norms, and non-smooth minimization using subgradient descent.