Parametric Models: Regression Estimators and Optimization

Related lectures (32)

Page 3 of 4

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

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

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.