Rebecca Morrison’s research focuses on probabilistic models of different types, including probabilistic graphical models to interpret and simplify large data sets, and Bayesian inference and reasoning to calibrate and validate predictive computational models. These topics are motivated by the goal to make reliable predictions of physical systems using computational models, physical constraints, and available data sets. Rebecca is also interested in dynamical systems and combinatorial game theory, and has worked on a number of applications at the intersection of physics-based and data-driven models, including combustion, epidemiology, satellites and reentry vehicles, and space weather.
CSCI 2820 - Linear Algebra with Computer Science Applications
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Spring 2019 / Fall 2019 / Fall 2022
Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues. Includes the basics of floating point computation and numerical linear algebra. Same as CSPB 2820.
CSCI 4802 - Data Science Team Companion Course
Primary Instructor
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Fall 2020 / Spring 2021
Gives students hands-on experience applying data science techniques and machine learning algorithms to real-world problems. Students work in small teams on internal challenges, many of which will be sponsored by local companies and organizations and will represent the university in larger teams for external challenges at the national and global level, such as those hosted by Kaggle. Students will be expected to participate in both internal and external challenges, attend meetings and present short presentations to the group when appropriate. Same as CSCI 5802.
CSCI 4830 - Special Topics in Computer Science
Primary Instructor
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Fall 2021 / Spring 2024
Covers topics of interest in computer science at the senior undergraduate level. Content varies from semester to semester. Only 9 credit hours from CSCI 4830 and/or CSCI 4831 can count toward Computer Science BS or BA.
CSCI 5646 - Numerical Linear Algebra
Primary Instructor
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Fall 2023
Offers direct and iterative solutions of linear systems. Also covers eigen value and eigenvector calculations, error analysis, and reduction by orthogonal transformation. A sound knowledge of basic linear algebra, experience with numerical computation, and programming experience is required.
CSCI 5802 - Data Science Team Companion Course
Primary Instructor
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Fall 2020 / Spring 2021
Gives students hands-on experience applying data science techniques and machine learning algorithms to real-world problems. Students work in small teams on internal challenges, many of which will be sponsored by local companies and organizations and will represent the university in larger teams for external challenges at the national and global level, such as those hosted by Kaggle. Students will be expected to participate in both internal and external challenges, attend meetings and present short presentations to the group when appropriate. Instructor consent required. Same as CSCI 4802.
CSCI 5822 - Probabilistic Models of Human and Machine Learning
Primary Instructor
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Spring 2021 / Spring 2023
Introduces a set of modeling techniques that have become a mainstay of modern artificial intelligence, cognitive science and machine learning research. These models provide essential tools for interpreting the statistical structure of large data sets and for explaining how intelligent agents analyze the vast amount of experience that accumulates through interactions with an unfamiliar environment. Recommended prerequisite: undergraduate course in probability and statistics.
CSCI 7000 - Current Topics in Computer Science
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Fall 2018 / Spring 2020 / Fall 2021 / Fall 2023 / Spring 2024
Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 18 total credit hours.
