Courses in Complex Systems (CSYS)

CSYS 095 - Special Topics

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Credits: 1 to 18

CSYS 096 - Special Topics

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Credits: 1 to 18

CSYS 195 - Intermediate Special Topics

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Credits: 1 to 18

CSYS 196 - Intermediate Special Topics

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Credits: 1 to 18

CSYS 205 - Software Engineering

Treatment of software engineering problems and principles, including documentation, information hiding, and module interface specification syntax and semantics. Requires participation in a team project. Students who receive credit for CSYS 205 may not receive credit for CSYS 208 or CSYS 209. Cross-listing: CS 205.

Credits: 3

CSYS 221 - Deterministic Modls Oper Rsch

The linear programming problem. Simplex algorithm, dual problem, sensitivity analysis, goal programming. Dynamic programming and network problems. Prerequisites: CSYS 124; CSYS 121 desirable. Cross-listing: MATH 221.

Credits: 3

CSYS 226 - Civil Engineering Systems Anyl

Linear programming, dynamic programming, network analysis, simulation; applications to scheduling, resource allocation routing, and a variety of civil engineering problems. Pre/co-requisites: Senior or graduate standing in CEE or instructor permission. Cross-listing: CE 226.

Credits: 3

CSYS 245 - Intelligent Transportation Sys

Introduction to Intelligent Transportation Systems (ITS), ITS user services, ITS applications, the National ITS architecture, ITS evaluation, and ITS standards. Pre/co-requisites: CE 140 or equivalent, instructor permission. Cross-listing: CE 245.

Credits: 3

CSYS 251 - Artificial Intelligence

Introduction to methods for realizing intelligent behavior in computers. Knowledge representation, planning, and learning. Selected applications such as natural language understanding and vision. Prerequisites: CS 103 or CS 123, CS 104 or CS 124, STAT 153 or equivalent. Cross-listing: CS 251.

Credits: 3

CSYS 253 - Appl Time Series & Forecasting

Autoregressive moving average (Box-Jenkins) models, autocorrelation, partial correlation, differencing for nonstationarity, computer modeling. Forecasting, seasonal or cyclic variation, transfer function and intervention analysis, spectral analysis. Prerequisite: CE 211 or CE 225; or CE 141 or CE 143 with instructor's permission. Cross-listing: STAT 253.

Credits: 3

CSYS 256 - Neural Computation

Introduction to artificial neural networks, their computational capabilities and limitations, and the algorithms used to train them. Statistical capacity, convergence theorems, backpropagation, reinforcement learning, generalization. Prerequisites: MATH 124 (or MATH 271), STAT 153 or equivalent, computer programming. Cross-listed: STAT 256/CS 256.

Credits: 3

CSYS 266 - Chaos,Fractals&Dynamical Syst

Discrete and continuous dynamical systems, Julia sets, the Mandelbrot set, period doubling, renormalization, Henon map, phase plane analysis and Lorenz equations. Corequisite: CSYS 271 or CSYS 230 or instructor's permission Cross-listing: MATH 266.

Credits: 3

CSYS 268 - Mathematical Biology&Ecology

Mathematical modeling in the life sciences. Topics include population modeling, dynamics of infectious diseases, reaction kinetics, wave phenomena in biology, and biological pattern formation. Prerequisites: CSYS 124, CSYS 230; or instructor's permission. Cross-listing: MATH 268.

Credits: 3

CSYS 295 - Advanced Special Topics

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Credits: 1 to 18

CSYS 296 - Advanced Special Topics

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Credits: 1 to 18

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-listing: MATH 300.

Credits: 3

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-listing: CS 302.

Credits: 3

CSYS 303 - Complex Networks

Detailed exploration of distribution, transportation, small-world, scale-free, social, biological, organizational networks; generative mechanisms; measurement and statistics of network properties; network dynamics; contagion processes. Students from all disciplines welcomed. Pre/co-requisites: Math 301/CSYS 301, Calculus, and Statistics required. Cross-listing: MATH 303.

Credits: 3

CSYS 312 - Adv Bioengineering Systems

Advanced bioengineering design and analysis for current biomedical problems spanning molecular, cell, tissue, organ, and whole body systems including their interactions and emergent behaviors. Cross-listed with ME 312.

Credits: 3

CSYS 350 - Multiscale Modeling

Computational modeling of the physics and dynamical behavior of matter composed of diverse length and time scales. Molecular simulation. Coarse-graining. Coupled atomistic/continuum methods. Cross-listing: ME 350.

Credits: 3

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-listings: BIOL 352, CS 352.

Credits: 3

CSYS 355 - Statistical Pattern Recogntn

Analysis of algorithms used for feature selection, density estimation, and pattern classification, including Bayes classifiers, maximum likelihood, nearest neighbors, kernels, discriminants, neural networks, and clustering. Prerequisites: STAT 241 or STAT 251 or instructor permission. Cross-listing: STAT 355/CS 355.

Credits: 3

CSYS 359 - Appld Artificial Neural Ntwrks

Introduction to articifial neural networks. A broad range of example algorithms are implemented in MATLAB. Research applications to real data are emphasized. Pre/co-requisites: STAT 223, CS 016/CE 011, or permission. Cross-listed: CE 359.

Credits: 1 to 3

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 223 or STAT 225; CS 016/CE 011 or permission. Cross-listings: STAT 369/CSYS 369.

Credits: 3

CSYS 395 - Special Topics

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Credits: 1 to 18