Concise Signal Models and Compressive Sensing

Related lectures (29)

Page 2 of 3

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

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

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

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

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

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

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

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