Lectures related to Untitled | EPFL Graph Search

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

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

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Covers the probabilistic model for linear regression and the importance of regularization techniques.