Math 396 / Math 400

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Ron Buckmire
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Math 396: SYLLABUS

The full hardcopy version of the syllabus for the class is available.

Course Zoom Info: passcode: Math396
Course Objectives

This course is designed to provide students with exposure to and the experience of the mathematical modeling of real-world phenomena by
  • exposing students to mathematical models from various scientific disciplines (economics, biology, physics, data science, etc)
  • allowing students to analyze, develop, test and/or implement mathematical models (that they choose or devise);
  • presenting students with a different mode of classroom instruction and student involvement where students see themselves as equal participants in the education process;
  • providing students with opportunities to practice and hone oral and written presentation skills of mathematical/technical content; and
  • developing students' collaboration skills by working in small teams towards a common goal;
Course Description:
This course is a project-oriented seminar in mathematical modeling. Concepts from calculus, linear algebra, differential equations and other areas of mathematics will be used to derive, describe and solve mathematical models from the life, physical, and social sciences. Familiarity with a programming language is desirable but not required. The goal of this course is to provide students with multiple examples of how mathematics can be used to explain various phenomena, to describe real-world situations and to make (more quantitatively) informed decisions.

Class Structure:
All instruction will occur remotely via videoconference. The first half of the semester will consist of class sessions where mathematical content is presented in the form of mathematical modeling techniques and examples of specific types of models used in various disciplines and context. Written assessment of the understanding of the concepts and mathematics will be done and students will hand in responses by scanning in written solutions and uploading PDFs in the Math 396 Spring 2021 Google Drive at The example models will include differential equation-based models used in modeling COVID-19 and/or cinematic box-office dynamics, probabilistic models used in economics to describe racial discrimination, discrete/network models used to describe social networks and a machine learning model. In the second half of the semester students will be expected to work together in small permanent groups to analyze a selected mathematical model and produce an implementation of their own which answers a question they have devised.
  • This class uses a grading contract. See the detailed syllabus PDF handout for more details.


Last Updated:  January 21, 2021