The class schedule is subject to change at any time. Such changes are not always reflected immediately on this page. Please check classes.uoregon.edu for the accurate, live schedule for the term.

All the other courses are described in the University of Oregon Catalog. To view graduate courses offered in previous years, please visit the Graduate Course History page.

Math Course Descriptions

616/617/618 Real Analysis

We will teach the courses (Math 616 and 617) based on the book Real Analysis:
Measure Theory, Integration, and Hilbert Spaces; and Functional Analysis: Introduction to Further Topics in Analysis, by Elias M. Stein and Rami Shakarchi. We start with the concrete Lebesque measure and Lebseque integration on $R^d$.
Then we get into the differentiation. By a quick tour of the Hilbert space, then we use it to develop abstract theory of measure
and integration. This is our plan for the fall quarter.

For winter quarter, we study linear functional analysis (Banach spaces, revisiting Hilbert spaces and bounded linear operators). We begin with $L^p$-spaces. We plan to cover some harmonic analysis. If time permits we will discuss distributions and Sobolev spaces.

647/648/649 Abstract Algebra

647: category theory, multilinear algebra, introduction to ring theory and module theory
648: ring theory, module theory, representation theory of finite groups

681/682/683 Advanced Algebra Series

The 681-683 sequence is aimed at giving a thorough introduction to Algebraic Geometry.
In the first term we will follow chapter 1 of Hartshorne’s “Algebraic Geometry”.

684/685/686 Advanced Analysis Series

MA 684: Introduction to Modular Forms
This course will provide an introduction to modular forms, which play a central role in modern number theory. In addition to algebraic and analytic number theory, this topic has ties to algebraic geometry and representation theory and beyond. We will cover standard aspects of modular forms, for example definitions of modular forms, cusp forms, and Eisenstein series; dimension formulas; Hecke operators; connections with elliptic curves; and (as time allows) connections with Galois representations, congruences, L-functions, and/or automorphic forms. The recommended prerequisites for this course are the 600-level algebra and analysis sequences (although the 500-level sequences will suffice most of the time), as well as complex analysis and Fourier analysis (at the very least, being very clear on the definitions of “holomorphic” and “meromorphic,” plus being aware of the notion of a Fourier expansion).

MA 685: Harmonic and Functional Analysis of Frames
A frame is a generalization of the concept of a basis to sets which are overcomplete. That is, frame expansions are in general not unique and instead they satisfy a certain stability condition. Although frames were introduced in 1950’s, this area has experienced a renewed interest in recent years with the advent of wavelets. In this course we plan to explore the following topics depending on the interest of students.

1) General frames and Riesz bases in Hilbert spaces: dual frames, canonical dual frames, Naimark’s dilation theorem.
2) Frames in finite dimensional spaces: equiangular frames, fusion frames, connections with algebraic combinatorics and Littlewood-Richardson tableaux.
3) Frames in infinite dimensional spaces: Kadison’s Pythagorean Theorem, characterization of frame norms with prescribed frame operator and the Schur-Horn theorem.
4) Frames and Riesz bases in shift-invariant spaces.
5) The solution of the long standing Kadison-Singer problem and its equivalent formulation in terms of the paving conjecture, the Feichtinger conjecture, and the Bourgain-Tzafriri conjecture.

690/691/692 Advanced Topology/Geometry Series

MA 691: Classifying spaces
Classifying spaces seem specialized at first, defined by finding a contractible space with a free group action and then taking the quotient. But they arise across algebraic topology and its applications, being necessary to understand characteristic classes, group cohomology, equivariant topology, and many topics in homotopy theory. We will start with basic theory, including the simplicial model and some basics of group cohomology. Further topics covered will depend on audience interest, and could include a) more advanced group cohomology possibly including its relationships with representation theory and/or fusion systems and/or focus on symmetric groups b) Borel equivariant cohomology or c) topology of Eilenberg-MacLane spaces or d) p-compact groups or e) Dijkgraaf-Witten theory (classifying spaces and field theory). The audience will be surveyed and/or will engage in some reality TV style competitions to decide which further topic(s).

Math Seminars

Math 607 Topological field theories and tensor categories

This will be an introductory course on topological quantum field theories from algebraic point of view. By definition such a theory is a symmetric tensor functor from the category of cobordisms to a symmetric tensor category. Thus we discuss the general theory and some examples of tensor categories (including Deligne’s categories). Some interesting examples in dimension 3 require modular tensor categories which are tensor but non-symmetric; this also will be discussed in the class.

Math 607 Computer Algebra

Introduction to sage/python. Experimental math; mathematical illustration; data visualization; object oriented programming; functional programming; computer algebra. Various topics and applications from graduate and undergraduate mathematics as well as “the real world”, all of which are an excuse to write code. Classes are in-class pair-programming workshops, not lectures. Suitable for students intending to use software for math research, and/or considering a career outside academia. Take this class instead of signing up for some random undergraduate programming course.

607 Applied Math II: Statistical Learning

This course will cover statistical and machine learning theory using foundational approaches (i.e. not neural-network based, as these will be the focus of the third course in the Applied Math sequence). These will include probability theory, regression, classification, kernel methods, mixture models and expectation maximization, as well as inference for sequential data using hidden Markov models and linear dynamical systems. Homework assignments will be a mix of pen-and-paper calculations together with implementations and applications of machine learning algorithms to real and synthetic data using Python. The main prerequisites for this course are calculus and linear algebra. Prior familiarity with probability and statistics or with coding will be helpful but is not necessary.