Courses
CSYS 5060. Advanced Evolutionary Robotics. 3 Credits.
Explores the automated design of autonomous machines using evolutionary algorithms. Covers relevant topics in evolutionary computation, artificial neural networks, robotics, simulation and xenobots. Students complete weekly programming assignments, formulate research a research hypothesis, and use their system to test that hypothesis. Credit not awarded for more than one of the following: CSYS 5060, CS 5060, CS 3060. Prerequisite: Graduate student. Cross-listed with: CS 5060.
CSYS 5540. Advanced Machine Learning. 3 Credits.
Provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (linear regression, logistic regression, neural networks, support vector machines, decision tree, ensemble models, random forest); unsupervised learning (clustering, dimensionality reduction, kernel methods); Also introduces deep learning such as convolutional neural networks and discusses recent applications. Credit not awarded for more than one of the following: CSYS 5540, CS 5540, CS 3540. Prerequisites: Knowledge of statistics as from STAT 2510, knowledge of linear algebra as from MATH 2522 or MATH 2544; Graduate student. Cross-listed with: CS 5540.
CSYS 5766. Gr Chaos,Fractals&Dynmcal Syst. 3 Credits.
Discrete and continuous dynamical systems, Julia sets, the Mandelbrot set, period doubling, renormalization, Henon map, phase plane analysis, and Lorenz equations. Credit not awarded for both CSYS 5766 and MATH 3766. Prerequisite: Graduate student or Instructor permission. Cross-listed with: MATH 5766.
CSYS 5870. Data Science I. 3 Credits.
Data harvesting, cleaning, and summarizing; working with non-traditional, non-numeric data (social network, natural language textual data, etc.); scientific visualization; advanced data pipelines with a practical focus on real datasets and developing good habits for rigorous and reproducible computational science; Project-based. Credit not awarded for both CSYS 5870 and CS 3870. Prerequisites: Knowledge of CS 1210 and either STAT 1410 or STAT 2430 required; knowledge of CS 2100 and MATH 2522 or MATH 2544 recommended; Graduate student or Instructor permission. Cross-listed with: STAT 5870, CS 5870.
CSYS 5909. Natural Language Processing. 3 Credits.
Provides a comprehensive, breadth-first introduction to natural language processing (NLP), an interdisciplinary field at the intersection of computer science, linguistics, and artificial intelligence. Students will explore both classical approaches and modern deep learning techniques, including large language models, through an integrative and hands-on learning experience. Project-based, emphasizing practical engagement with real-world textual datasets and the development of strong research practices for rigorous research projects. Prerequisite: Programming experience in Python. Cross-listed with: CS 5909.
CSYS 5990. Special Topics. 1-18 Credits.
See Schedule of Courses for specific titles.
CSYS 5993. Independent Study. 1-18 Credits.
A course which is tailored to fit the interests of a specific student, which occurs outside the traditional classroom/laboratory setting under the supervision of a faculty member, for which credit is awarded. Offered at department discretion.
CSYS 6020. Modeling Complex Systems I. 3 Credits.
Integrative breadth-first introduction to computational methods for modeling complex systems; dynamical systems, numerical methods, cellular automata, agent-based computing, game theory, genetic algorithms, artificial neural networks, and complex networks. Semester team-based project. Pre/Co-requisites: Computer programming in any language; calculus, linear algebra recommended. Cross-listed with: CS 6020.
CSYS 6021. Modeling Complex Systems II. 3 Credits.
Deep dive in state-of-the-art mathematical and computational methods for modeling complex systems; model theory, branching processes, probability generating functions, message passing, master equations, event-driven simulations, Gillespie algorithms, composition-rejection algorithms. Prerequisites: CS 6020 or CSYS 6020. Cross-listed with: CS 6021.
CSYS 6391. Master's Thesis Research. 1-18 Credits.
Master's thesis research under the supervision of a graduate faculty member. Prerequisite: Instructor permission.
CSYS 6392. Master's Project Research. 1-6 Credits.
Masters project under the supervision of a graduate faculty member. Prerequisite: Instructor permission.
CSYS 6520. Evolutionary Computation. 3 Credits.
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, statistics. Cross-listed with: CS 6520.
CSYS 6540. Deep Learning. 3 Credits.
Introduction to Deep Learning algorithms and applications, including basic neural networks, convolutional neural networks, recurrent neural networks, deep unsupervised learning, generative adversarial networks and deep reinforcement learning. Includes a semester team-based project. Prerequisite: CS 3540. Cross-listed with: CS 6540.
CSYS 6570. Social Computing Systems. 3 Credits.
Social computing systems include online social networks, microblogging systems, social recommendation platforms, etc. Via a research-centric lens, explores the underlying nature/structure of social computing systems, studies various issues that plague them, and explores the methods by which researchers investigate such systems. Prerequisites: Proficiency in graph theory and computer programming (preferred language Python); knowledge of CS 3240 (or equivalent) and CS 2300 (or equivalent) assumed. Cross-listed with: CS 6570.
CSYS 6701. Principles of Complex Systms 1. 3 Credits.
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, statistics required; linear algebra, differential equations, computer programming recommended. Cross-listed with: MATH 6701.
CSYS 6713. Principles of Complex Systms 2. 3 Credits.
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: Calculus, statistics required. Cross-listed with: MATH 6713.
CSYS 6870. Data Science II. 3 Credits.
Advanced data analysis, collection, and filtering; statistical modeling, monte carlo statistical methods, and in particular Bayesian data analysis, including necessary probabilistic background material; a practical focus on real datasets and developing good habits for rigorous and reproducible computational science. Prerequisites: STAT 5870, CS 5870, CSYS 5870, or Instructor permission. Cross-listed with: CS 6870, STAT 6870.
CSYS 6990. Special Topics. 1-18 Credits.
See Schedule of Courses for specific titles.
CSYS 6991. Internship. 1-18 Credits.
On-site supervised work experience combined with a structured academic learning plan directed by a faculty member or a faculty-staff team in which a faculty member is the instructor of record, for which academic credit is awarded. Offered at department discretion.
CSYS 6993. Independent Study. 1-18 Credits.
A course which is tailored to fit the interests of a specific student, which occurs outside the traditional classroom/laboratory setting under the supervision of a faculty member, for which credit is awarded. Offered at department discretion.
CSYS 6995. Graduate Independent Research. 1-18 Credits.
Graduate student work on individual or small team research projects under the supervision of a faculty member, for which credit is awarded. Offered at department discretion.
CSYS 7491. Doctoral Dissertation Research. 1-18 Credits.
Research for the Doctoral Dissertation.
CSYS 7980. Applied Geostatistics. 3 Credits.
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. Prerequisites: Programming skills (such as in Python or Matlab) and content knowledge of multivariate statistics (such as STAT 5230) are assumed. Cross-listed with: CEE 7980, STAT 7980.
CSYS 7990. Special Topics. 1-18 Credits.
See Schedule of Courses for specific titles.
CSYS 7995. Graduate Independent Research. 1-18 Credits.
Graduate student work on individual or small team research projects under the supervision of a faculty member, for which credit is awarded. Offered at department discretion.
