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Cox, Rachel Suzanne-Tutmaher

Lecturer

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Teaching

courses taught

  • APPM 1235 - Pre-Calculus for Engineers
    Primary Instructor - Spring 2018 / Fall 2018
    Prepares students for the challenging content and pace of the calculus sequence required for all engineering majors. Covers algebra, trigonometry and selected topics in analytical geometry. Prepares students for the calculus courses offered for engineering students. Requires students to engage in rigorous work sessions as they review topics that they must be comfortable with to pursue engineering course work. Structured to accustom students to the pace and culture of learning encountered in engineering math courses. For more information about the math placement referred to in the "Enrollment Requirements", please contact your academic advisor. Degree credit not granted for this course and MATH 1021 or MATH 1150. Formerly GEEN 1235.
  • COEN 1236 - Precalculus Work Group
    Primary Instructor - Spring 2018
    Develops and enhances problem solving skills for students enrolled in APPM 1235. Course is conducted in a collaborative learning environment with students working in groups under the guide of a facilitator.
  • CSCI 2820 - Linear Algebra with Computer Science Applications
    Primary Instructor - Spring 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.
  • CSCI 2824 - Discrete Structures
    Primary Instructor - Fall 2018 / Spring 2019 / Fall 2019 / Spring 2021 / Fall 2021
    Covers foundational materials for computer science that is often assumed in advanced courses. Topics include set theory, Boolean algebra, functions and relations, graphs, propositional and predicate calculus, proofs, mathematical induction, recurrence relations, combinatorics, discrete probability. Focuses on examples based on diverse applications of computer science. Same as CSPB 2824.
  • CSCI 2830 - Special Topics in Computer Science
    Primary Instructor - Spring 2019
    Covers topics of interest in computer science at the sophomore level. Content varies from semester to semester. Does not count as Computer Science credit for the Computer Science BA, BS or minor. May be repeated up to 9 total credit hours.
  • CSCI 2834 - Discrete Structures Workgroup
    Primary Instructor - Spring 2021 / Fall 2021
    Provides additional problem-solving practice and guidance for students enrolled in CSCI 2824. Students work in a collaborative environment to further develop their problem-solving skills with the assistance of facilitators. Does not count as Computer Science credit for Computer Science majors or minors.
  • CSCI 3022 - Introduction to Data Science with Probability and Statistics
    Primary Instructor - Fall 2019 / Spring 2020 / Summer 2021
    Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
  • CSCI 3104 - Algorithms
    Primary Instructor - Spring 2021 / Spring 2024
    Covers the fundamentals of algorithms and various algorithmic strategies, including time and space complexity, sorting algorithms, recurrence relations, divide and conquer algorithms, greedy algorithms, dynamic programming, linear programming, graph algorithms, problems in P and NP, and approximation algorithms. Same as CSPB 3104.
  • CSCI 3202 - Introduction to Artificial Intelligence
    Primary Instructor - Spring 2020 / Fall 2021 / Spring 2022 / Spring 2024
    Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language. Same as CSPB 3202.
  • STAT 4010 - Statistical Methods and Applications II
    Primary Instructor - Spring 2024
    Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 5010.
  • STAT 5010 - Statistical Methods and Applications II
    Primary Instructor - Spring 2024
    Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 4010.

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