ENEB345

Prerequisite: MATH141. Restriction: Must be in the Embedded Systems & Internet of Things program and must receive permission from the Embedded Systems & Internet of Things program. This is a foundational course on probability and statistics for data science and connected embedded systems. This covers basic statistics and probability theory, including random variables, standard distributions, moments, law of large numbers and central limit theorem, sampling methods, estimation of parameters, testing of hypotheses. The course also includes the mathematical theory of randomness, and applications to big data analysis and analysis in the presence of uncertainty, and applications to machine learning algorithms.