Comparing L1 and L0 + Greedy algorithms

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Linear and Ridge Regression

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

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Data-Driven Modeling: Regression

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

Kernel Methods: Understanding Overfitting and Model Selection

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

Geometry of the Lasso

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Polynomial Regression and Gradient Descent

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