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This course is concerned with the building, using, and critiquing of mathematical models. In constructing models, one translates scientific questions into mathematical language, and thereby one aims to explain the scientific phenomena under investigation. Models can be simple or very complex, easy to understand or extremely difficult to analyze. In this course, the models will be data-driven: we will study regression models (including the generalized linear model), time series models, KNN models, principal components analysis, decision trees, and cluster analysis.

A community building weekly seminar introducing S-STEM scholars to the different directions of research pursued in the Department of Mathematics and Statistics and applications of mathematics and statistics in industry. During fall and spring semesters, the seminar will be a one-credit course that S-STEM scholars can register and receive a grade for. Embedded within the seminars, students will work on short- and long-term goal setting for their program and career aspirations, routinely conduct self-reflections, and have regular peer and facilitator feedback.

This class is designed to help students review and prepare for the GRE Mathematics subject exam, which is a required exam for entrance into many PhD programs in mathematics. Students should have completed the three courses in calculus, a course in linear algebra, and have some familiarity with differential equations. The focus will be on solving problems based on the core material covered in the exam. Students are expected to do practice problems before each meeting and discuss the solutions in class.

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This course covers probabilistic aspects of risk management intended for those familiar with probability theory. Games of chance are used as models of real-world situations of decision-making under uncertainty. Students will gain a deeper understanding of the mathematical foundations of probability theory and stochastic processes. They will apply concepts in game theory and probability theory to real-world problems in risk management.

This course is an introduction to knot theory. Knot theory is currently an active area of research in low-dimensional topology, with all kinds of connections to other mathematical fields, including geometry, algebra, physics, combinatorics, and number theory.

This course explores various approaches to geometry, as we trace the evolution of mathematical thinking and rigor from ancient to modern: constructions with straight-edge and compass, axiomatic approach of Euclid and Hilbert, analytic geometry via linear algebra, and Klein?s approach using symmetries and transformations. This will open the doors to many non-Euclidean flavors of geometry, where projective geometry will be studied in some detail.

We learn how to build, use, and critique mathematical models. In modeling we translate scientific questions into mathematical language, and thereby we aim to explain the scientific phenomena under investigation. Models can be simple or very complex, easy to understand or extremely difficult to analyze. We introduce some classic models from different branches of science that serve as prototypes for all models. Student groups will be formed to investigate a modeling problem themselves and each group will report its findings to the class in a final presentation.

We learn how to build, use, and critique mathematical models. In modeling we translate scientific questions into mathematical language, and thereby we aim to explain the scientific phenomena under investigation. Models can be simple or very complex, easy to understand or extremely difficult to analyze. We introduce some classic models from different branches of science that serve as prototypes for all models. Student groups will be formed to investigate a modeling problem themselves and each group will report its findings to the class in a final presentation.