MTH 396 Prerequisites:

MTH 396 Prerequisites: Completion of Tier I Writing Requirement, MTH 309, MTH 310, and MTH 320 (or the honors equivalents, or approval of department) and approval of the department. Typically the department expects a cumulative GPA of at least 2.0 and an average of at least 2.0 across MTH 309, MTH 310 and MTH 320. Note: Email notification will be given once your override has been issued.

MTH 496 Prerequisites:

Completion of Tier I Writing Requirement and approval of the department. Typically the department expects students to have completed MTH 309, MTH 310, and MTH 320 (or the honors equivalents) with cumulative GPA of at least 2.0 and an average of at least 2.0 across MTH 309, MTH 310 and MTH 320. Additional prerequisite courses may be required and can be found in the descriptions below. Note: Email notification will be given once your override has been issued.

Fall 2021: MTH 496 Section 1 - Fourier Analysis

Instructor: Jun Kitagawa

The course will cover various modes of convergence for Fourier series on the circle, including pointwise, uniform, and L^2 convergence. There will also be some discussion of the Fourier transform on R^n, and applications to the heat equation.

Prerequisites: MTH 309 and MTH 320 and Department Approval

Spring 2022: MTH 496 Section 1 - Machine Learning

Instructor: Ekaterina Rapunchik

This course provides a broad introduction to Machine Learning. Topics include supervised learning ( support vector machines, kernels, neural networks) and unsupervised learning (clustering, dimensionality reduction, etc.). We will also discuss the different tasks of machine learning: classification, regression, clustering, density estimation and dimensionality reduction. During the last part of the course, we will discuss some of the recent approaches to machine learning, including those involving the graphical framework.

Prerequisites: MTH 309 and Department Approval (CSE 231 or CMSE 201 recommended)