MATH 215 - Applied Probability Theory (4)

An introductory course in probability covering the basic concepts and applications in a variety of scenarios. Students will learn both theory and computational tools necessary to understand the randomness and uncertainty that arise in various fields such as data science, machine learning, social science, health science, engineering, economics, and finance. Topics include basic probability concepts, combinatorics, conditional probability, independence, discrete random variables, continuous distributions, joint distributions, covariance, correlation, conditional expectation, moment-generating functions, Markov and Chebyshev inequalities, the law of large numbers, and central limit theorem. The course focuses on data-based applications and involves computations in the software program R, utilizing real data sets. This course aims to provide a strong foundation for future study of statistical inference and modeling, machine learning, computer science, econometrics, and other subjects where probability is needed. Satisfies the General Education Mathematics requirement. Offered: Every term.