This course reviews and correlates the courses in the mathematics major. Each student is responsible for preparing the review of one area. Students also read papers from contemporary mathematics journals and present them to the class. The course uses the ETS mathematics major exam.

Prerequisites

MATH 2210; MATH 2410

Every Spring

Previously: MATH 490

An introduction to topological structures from point-set, differential, algebraic, and combinatorial points of view. Topics include continuity, connectedness, compactness, separation, dimension, homeomorphism, homology, homotopy, and classification of surfaces.

Prerequisites

MATH 2210; MATH 2410

Every other Spring, odd years

Previously: MATH 345

A survey of the classical algebraic structures taking an axiomatic approach. Deals with the theory of groups and rings and associated structures, including subgroups, factor groups, direct sums of groups or rings, quotient rings, polynomical rings, ideals, and fields.

Prerequisites

MATH 2210; MATH 2410

Every other Fall, even years

Previously: MATH 340

This course develops the logical foundations underlying the calculus of real-valued functions of a single real variable. Topics include limits, continuity, uniform continuity, derivatives and integrals, sequences and series of numbers and functions, convergence, and uniform convergence.

Prerequisites

MATH 2210; MATH 2410

Occasionally

Previously: MATH 350

A study of the concepts of calculus for functions with domain and range in the complex numbers. The concepts are limits, continuity, derivatives, integrals, sequences, and series. Topics include Cauchy-Riemann equations, analytic functions, contour integrals, Cauchy integral formulas, Taylor and Laurent series, and special functions.

Prerequisites

MATH 2210; MATH 2410

Occasionally

Previously: MATH 355

Students will learn about supervised and unsupervised learning (K-Means Clustering). They will be able to assess model accuracy. Students will be able to perform classification, in particular they will learn about Generative Models for Classification and Generalized Linear Models GLMs. Students will be able to perform Cross-Validation: Training and Test Set. Students will be able to make variable selection by applying Ridge or LASSO Regression.

Previously: MATH 325