Fall 2022 - Summer 2023 Courses

Note: The one-letter day abbreviations are M,T,W,R,F. Late afternoon classes begin at or after 4:30 pm. Evening classes begin at or after 6:00 pm. Contact the ACM director before signing up for classes.

Fall 2022

Math 551: Advanced Calculus I

M 10:00 - 10:50 AM, TR 9:30 AM - 10:45 AM, Prof. Alan Wiggins

Advanced Calculus is the rigorous study of the concepts of limit, differentiation, and integration. In this course, we dig deeper into the historical developments of these concepts and deduce many principles from basic axioms. Students will practice a serious application of logic and proof techniques in the course. The basic ideas develop in this course constitute a strong foundation for any further course in mathematics. Previous experience in first-year calculus and proof methods will be helpful.

Satisfies: Core A: Analysis

Offered: Every Fall

Prerequisites: Introductory Linear Algebra class (e.g. Math 227), Differential Equations with Linear Algebra (e.g. Math 228), and Introduction to Proofs class (e.g. Math 300).

Math 562: Math Modeling

MW,  11:00 AM - 12:15 PM, in-person, Profs. Hristova and Kim

The processes of constructing, implementing, and evaluating mathematical models of "real world" phenomena are investigated. Optimization models, dynamic models, and probability models are considered. The goal is to learn how to model phenomena arising in biology, social sciences, physics etc. and rigorously study them using mathematics. Matlab will be used extensively to solve problems that are not tractable by hand.

Satisfies: Core C: Modeling

Offered: Every Fall

Prerequisites: Multivariable Calculus (e.g. Math 215) and Differential Equations with Linear Algebra (e.g. Math 228).

Math 572: Numerical Analysis

TR, 6:00 PM - 7:15 PM, in-person, Prof. Viswanathan

Numerical Analysis is the study of algorithms that approximate solutions to many problems in mathematics. In this course, we study how computers approximate and store numbers and how round off error can affect computations. We develop and analyze algorithms to approximate solutions to mathematical problems including finding roots of functions, solving definite integrals, interpolating data by polynomials and splines, numerical differentiation, solving systems of linear equations, and solving differential equations. Some programming experience will be helpful. 

Satisfies: Core B: Numerical Methods

Offered: Every Fall

Prerequisites: Introductory linear algebra class (e.g. Math 227)

Stat 530: Applied Regression Analysis

TR, 04:30 PM - 05:45 PM and 6:00 PM - 7:15 PM Prof. Li

Topics include single variable linear regression, multiple linear regression and polynomial regression. Model checking techniques based on analysis of residuals will be emphasized. Remedies to model inadequacies such as transformations will be covered. Basic time series analysis and forecasting using moving averages and autoregressive models with prediction errors are covered. Statistical packages will be used.

Satisfies: Modeling Specialization C: Statistical Models

Offered: Every fall and some winters

Prerequisites: Introductory statistics class (e.g. Stat 325, IMSE 317)

Winter 2023 

Math 523: Applied Linear Algebra

MW 6:00 - 7:15 PM, in-person, Prof. Viswanathan

Review of elementary linear algebra concepts followed by the study of Gaussian elimination and solutions of systems of equations, matrix factorizations, inverses, vector spaces and subspaces, linear transformations, determinants, eigenspaces and eigen analysis, singular value decomposition. Applications may include discrete Fourier analysis, optimization, solutions of systems of differential equations and data science. 

Satisfies: Modeling Specialization A: Linear and Discrete Models 

Offered: Occasionally

Prerequisites: Introductory Linear Algebra class (e.g. Math 227)

Math 525: Mathematical Statistics II 

T (in-person) R(online) 4:30 - 5:45 PM, Prof. Dabkowski

Statistical inference is the process of drawing conclusions about a given population based on observed data from random samples. There are many approaches to perform inference, which enables us to argue from the particular observations in a sample to the general case. The two main classes of inference problems are estimation of parameters and testing hypotheses about the value of the parameters. In this course we will study the fundamentals of statistical inference and we expect that students will be able to use them for more complex inferential challenges.

Satisfies: Modeling Specialization C: Statistical Models 

Offered: Every other year

Prerequisites: Introductory statistics (e.g. Stat 325)

Stat 530: Applied Regression Analysis 

MW, 9:30 - 10:45 AM, in-person, Prof. Pokhrel

Topics include single variable linear regression, multiple linear regression and polynomial regression. Model checking techniques based on analysis of residuals will be emphasized. Remedies to model inadequacies such as transformations will be covered. Basic time series analysis and forecasting using moving averages and autoregressive models with prediction errors are covered. Statistical packages will be used.

Satisfies: Modeling Specialization C: Statistical Models

Offered: Every fall and some winters

Prerequisites: Introductory statistics class (e.g. Stat 325, IMSE 317)

STAT 550 Multivariate Statistical Analysis

MW 4:30 - 5:45 PM, in-person, Prof. Li

An introduction to commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. Topics include: multivariate analysis of variance, multivariate regression, principal components and factor analysis, canonical correlation, and discriminant analysis.

Satisfies: Modeling Specialization Area C: Statistical Models

Offered: Every other year

Prerequisites: Stat 530

Summer 2023

Math 520: Stochastic Processes

Late Afternoon, Prof. Kim

Review of distribution theory. Introduction to stochastic processes, Markov chains and Markov processes, counting, and Poisson and Gaussian processes. Applications to queuing theory.

Satisfies: Modeling Specialization C: Statistical Models

Offered: Occasionally

Prerequisites: Introductory probability or statistics class (Math 325, Stat 325, IMSE 317, etc.)