MATH 106 Stochastic Processes & Uncertainty Quantification
Annually, Winter term. Stochastic modeling and uncertainty quantification are central to the study of many problems in physics, engineering, finance, evolutionary biology and medicine. This course introduces theoretical concepts in probability theory and key methods for stochastic processes and uncertainty quantification. MATH 106 is an approved elective for the QBS Masters of Science degree in Health Data Science.
The topics of this course will alternate between odd and even years. In even years, topics will include basic concepts of probability, generating functions, Markov chains, random walks, Markov and Non-Markov processes, and diffusion theory. Applications to the natural sciences will be made. In odd years, the course will focus on data-driven methods, with applications in data science, machine learning, and numerical weather prediction. Topics will include statistical inference, random sampling, stochastic processes, polynomial chaos, and data assimilation. The course will also introduce standard computation libraries in MATLAB and Python.
Instructor
Lee