University of Vermont

UVM Course Directory

Term: Fall 2019

Subject: Complex Systems

CSYS 295 - Advanced Special Topics

See Schedule of Courses for specific titles.

CSYS 300 - Principles of Complex Systems

Introduction to fundamental concepts of complex systems. Topics include: emergence, scaling phenomena and mechanisms, multi-scale systems, failure, robustness, collective social phenomena, complex networks. Students from all disciplines welcomed. Pre/co-requisites: calculus and statistics required; Linear algebra, differential equations, and computer programming recommended but not required. Cross-listed with: MATH 300.

CSYS 302 - Modeling Complex Systems

Integrative breadth-first introduction to computational methods for modeling complex systems; numerical methods, cellular automata, agent-based computing, game theory, genetic algorithms, artificial neural networks, and complex networks. Pre/co-requisites: Computer programming in any language; calculus. Linear algebra recommended. Cross-listed with: CS 302.

CSYS 352 - Evolutionary Computation

Theory and practice of biologically-inspired search strategies including genetic algorithms, genetic programming, and evolution strategies. Applications include optimization, parameter estimation, and model identification. Significant project. Students from multiple disciplines encouraged. Pre/co-requisites: Familiarity with programming, probability, and statistics. Cross-listed with: BIOL 352, CS 352.

CSYS 369 - Applied Geostatistics

Introduction to the theory of regionalized variables, geostatistics (kriging techniques): special topics in multivariate analysis; Applications to real data subject to spatial variation are emphasized. Pre/co-requisites: STAT Prerequisites: STAT 223 or STAT 225; CS 020 or CS 021; or Instructor permission. Cross-listed with: CE 369, STAT 369.

CSYS 391 - Master's Thesis Research

Masters thesis research under the supervision of a graduate faculty member. Prerequisite: Instructor permission.

CSYS 392 - Master's Project

Masters Project under the supervision of a graduate faculty member. Prerequisite: Instructor permission.