Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.
Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
