Machine learning is the design of algorithms that routinely learn and adapt with use to discover hidden properties, patterns, and trends in complex data. This is a semester course on foundational methods, probability theory, and statistical methods, focusing on data classification and pattern recognition, formulating and testing hypotheses, and statistical forecasting of trends in data that highlight potential tradeoffs and decision options by stakeholders. Topics include discrete and continuous random variables, the algebra of random variables, independence, central limit theorems, Gaussian distributions (univariate and multivariate forms). Topics in statistics focus on regression theory and hypothesis testing.