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.

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.

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.

Master's thesis research under the supervision of a graduate faculty member. Prerequisite: Instructor permission.

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.

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.

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.

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Research for the Doctoral Dissertation.