MATH 186 Network Models
This course treats network and graphical models arising especially in biological and cognitive sciences. Methods include networks graphs and matrices; probability conditional probability and Markov chains; discrete-time dynamics and recurrent neural networks; Bayesian statistical inference on graphical models; and optimization on graphs including dynamic programming. In the computing laboratory component (a separately-scheduled 1.5 hour session) students will learn to use MATLAB to build and analyze models. Students will complete projects in each major area of the course. Calculus is not a pre-requisite. While open to all students this course is intended as an alternative to calculus as a first course in college-level mathematics.