Semester.ly

Johns Hopkins University | EN.520.622

Principles of Complex Networked Systems

3.0

credits

Average Course Rating

(4.5)

By employing fundamental concepts from diverse areas of research, such as statistics, signal processing, biophysics, biochemistry, cell biology, and epidemiology, this course introduces a multidisciplinary and rigorous approach to the modeling and computational analysis of complex interaction networks. Topics to be covered include: overview of complex nonlinear interaction networks and their applications, graph-theoretic representations of network topology and stoichiometry, stochastic modeling of dynamic processes on complex networks and master equations, Langevin, Poisson, Fokker-Plank, and moment closure approximations, exact and approximate Monte Carlo simulation techniques, time-scale separation approaches, deterministic and stochastic sensitivity analysis techniques, network thermodynamics, and reverse engineering approaches for inferring network models from data.

Fall 2012

(4.67)

Fall 2013

(4.33)

Fall 2012

Professor: John Goutsias

(4.67)

Students enjoyed the interesting topic, which covered a newly developing field of research. They also said the professor effectively explained the material and gave engaging lectures. Students had difficulty understanding some of the algorithms or how to use them. The course would be improved by having a discussion section for students to ask more questions. This course is a good introduction to an interesting field and has a fairly light workload, which involves a project at the end of the semester.

Fall 2013

Professor: John Goutsias

(4.33)

Students thought that the best aspects of this course were the interesting topics and the professor’s thorough lecturing style. Students found the materials chal enging but refreshingly so, although students did complain that the homework load was intense and the difficult materials sometimes led to confusion. Students suggested that the course be broken up into multiple shorter sessions to give them time to review the materials. They also wanted solutions to homework and the exams posted so they could use these materials when studying. Prospective students should have a solid understanding of probability, statistics, and signal processing and be prepared to take on a heavy course load.

Lecture Sections

(01)

No location info
J. Goutsias
13:30 - 14:45