- Schedule of Classes - June 25, 2020 7:14PM EDT
- Course Catalog - June 25, 2020 7:15PM EDT
Course information provided by the Courses of Study 2019-2020.
Course addresses a collection of topics relevant to the modeling, analysis, simulation, and optimization of large complex multi-agent systems. Course provides a standalone introduction to discrete-time Markov chains; covers the Metropolis algorithm and its generalizations; gives an introduction to the theory of genetic algorithms; and provides an introduction to evolutionary game theory, including the ESS concept, replicator dynamics, and dynamic probabilistic approaches.
When Offered Spring.
Prerequisites/Corequisites Prerequisite: ECE 3100 or a strong familiarity with discrete probability.
- Develop an understanding of discrete-time Markov chains with countable state spaces.
- Learn about the historical development of various random-search techniques.
- Attain a fairly deep understanding of the theory of genetic algorithms.
- Attain a basic understanding of evolutionary game theory and its importance in modeling and analysis of modern large-scale systems.
Regular Academic Session.
Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- MWThurston Hall 203
- Jan 21 - May 5, 2020
Instruction Mode: Hybrid - Online & In Person
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