Untitled

Related lectures (29)

Page 3 of 3

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

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

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

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

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

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