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 semester-long course explores foundational methods, probability theory, and statistical methods, focusing on classifying data, recognizing patterns, formulating and testing hypotheses, and forecasting statistical trends 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, and Gaussian distributions (univariate and multivariate forms). Topics in statistics focus on regression theory and hypothesis testing.