Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.
Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.
Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.
Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.
