Courses | Department of Mathematics


Schedule of Classes

Catalog Course Descriptions

View the Course Descriptions from the University Catalog

Spring 2024 Graduate Courses

  • MATH 4510/5700-Abstract Algebra II & Selected Topics in Contemporary Mathematics: William Cherry (Syllabus Math 4510/5700)

  • MATH 5290-Numerical Methods: Yanyan He (MATH 5290)

  • MATH 6010-Logic and Foundations: John Krueger

    • The topic of this course is set theory, and more specifically, the theory of trees. After reviewing some basic set theory, including ordinal and cardinal numbers, cardinal arithmetic, and club and stationary sets, we concentrate on the subject of trees, linear orders, and the connections of these objects with topology. We will study Aronszajn, Suslin, and Kurepa trees and lines, and set theoretic principles such as Martin's axiom and diamond principles which have implications concerning the existence of such trees and their properties. Prerequisites of the course include some familiarity with ordinal and cardinal numbers and Math 5610. Required work will include solving homework problems and presenting solutions in class. There will be no exams. Grading is based on attendance and homework.

  • MATH 6110-Topics in Analysis: Kirill Lazebnik

    • This course will explore some phenomena in the field known as "complex dynamics". One starting point is the following definition. Let f_c(z):=z^2+c for z and c complex numbers, and consider what happens when iterating the function f_c at a point z, in other words consider the sequence (z^2+c, (z^2+c)^2+c, ((z^2+c)^2+c)^2+c, …). The set of z having the property that perturbing z slightly results in the behavior of the above sequence changing predictably is known as the Fatou set, and the complement of the Fatou set is known as the Julia set (making this precise is not too difficult). The Fatou and Julia sets exhibit fractal behavior, and they depend on the parameter c in a surprisingly intricate manner. We will attempt to explain this dependence from more or less first principles, incorporating a diverse set of tools along the way.

  • MATH 6710-Applied Math (Bioinformatics): Serdar Bozdag

    • In this course, machine learning methods on graphs, particularly biological graphs will be covered. Graphs are natural data structures to represent multiple data modalities, which are currently abundant in multiple domains such as social networks and biology. We will cover shallow machine learning methods such as DeepWalk, node2vec and deep learning methods such as Graph Neural Networks and Graph Attention Networks. Discussion on more advanced methods for more complex graphs such as multiplex heterogeneous graphs will be discussed. Prior knowledge in programming and machine learning is highly recommended. No prior knowledge in biology is needed.