MATH 109 - Applied and Computational Probability

Prerequisite: MATH 101 with a C or better or permission of instructor. Recommended: MATH 107
Introduction to applied probability and stochastic processes with real-world applications across various fields, including business, finance, actuarial science, engineering, and data science. Topics include Bayesian methods, Monte Carlo techniques, Markov chains, stochastic processes, random processes, and simulations. Additional topics may include reliability analysis, signal processing, pattern recognition, dynamic programming, time series analysis, and survival analysis. Emphasis is on computational and simulation-based approaches to probabilistic modeling and decision-making.

Units: 3

Course Typically Offered: Fall