Error Decomposition and Regression Methods

Related lectures (31)

Page 3 of 4

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

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

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

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

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

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

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