Course Requirements and Descriptions

WISeNet Core Courses:

Other Recommended Courses:

  • ME 546: Intelligent Systems
  • ME 344: Control of Dynamic Systems
  • ENVIRON 734: Watershed Hydrology
  • ENVIRON 856: Environmental Fluid Mechanics
  • ENVIRON 764: Applied Differential Equations in Enviromental Sciences
  • CNS201S: Topics on Complexity in the Environment: hydrodynamics and carbon transport
  • COMPSCI 571: Machine Learning
  • COMPSCI 570: Artificial Intelligence
  • STA 502: Bayesian Inference and Decision
  • ME 541: Intermediate Dynamics
  • COMPSCI 534: Computational Complexity
  • COMPSCI 634: Computational Geometry
  • BIOLOGY 729: Ecological Forecasting Workshop

View the complete catalogue of Duke courses.

WISeNet Core Course Descriptions

ME 555 - Intelligent Sensors

An introductory course on learning and intelligent-systems techniques for the modeling and control of dynamic sensor networks. Review of dynamical systems theory, numerical optimization, probability, and computational complexity analysis. Interdisciplinary methods for intelligent sensor fusion, sensor management, and mobile sensor navigation and control. Topics include neural networks, Bayesian networks, genetic algorithms as they apply to problems drawn from intelligent sensor placement for environmental monitoring, sensor path planning, sensing-and-pursuit games, target classification, and search-and-communications in heterogeneous sensor networks. (3 credit hours)

Offered Spring 2013, Spring 2015

CE 690 - Optimal Environmental Sensing

Modeling of sensor-environment interaction in soils, plants, and the lower atmosphere.  Foundations of signal processing, instrumentation theory and practice, as they apply to the analysis of environmental, intermittent time series.  Design of energy-efficient wireless sensor networks able to deal with temporal intermittence patterns.  Topics include genesis of intermittency in dynamical and complex systems, intermittency in statistical measures, hydrologic, radiative, and turbulence time series, and environmental prediction. (3 credit hours)

Offered Fall 2012, Fall 2013

COMPSCI 570 - Artificial Intelligence

Concepts from probability and statistics, computational complexity and intractability, planning algorithms, and approximation techniques.  Theory and algorithms from reinforcement learning, Bayesian inference, and information theory.  Topics include POMDPs, planning and sensing, with an emphasis on hierarchy and approximation methods, and applications in non-myopic multi-aspect sensing. (3 credit hours)

Offered Fall 2012, Fall 2013

ME 759 - WISeNet Seminar

Weekly seminar on cross-cutting topics in environmental science, systems theory, and machine learning.  The course includes a required one-hour, pre-seminar lecture on the seminar topic with the seminar held the following week. Offered each spring starting spring 2013. (1 credit hour)