Practice statistical inference for machine learning readiness.

This Gradus course builds statistical inference from probability models through asymptotics, estimation, testing, Bayesian reasoning, regression, graphical models, and classification.

Course topics

• Probability laws, random variables, moments, inequalities, and concentration
• Modes of convergence, laws of large numbers, central limit theorem, and delta method
• Statistical models, estimators, bootstrap, likelihood, and hypothesis testing
• Bayesian inference, decision theory, regression, dependence, and causal inference
• Graphical models, nonparametric inference, classification, and Monte Carlo methods

Published package

The remote course ID is statistical_inference_wasserman. It includes 22 chapters, 83 lessons, and 286 concepts.