Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.
Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
