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## Measure Theory for Scientists and Engineers

An introduction to measure theory and Lebesgue integration with a focus on topics that tend to be of the most utility in science and engineering. The three course series MATH 6579, 6580, and 6221 is designed to provide a high level mathematical background for engineers and scientists.

This course is equivalent to MATH 6337. Students should not be able to obtain credit for both MATH 6579 and MATH 6337.

## Mathematical Foundations of Data Science

Modern data science methods and the mathematical foundations: linear regression, classification and clustering, kernel methods, regression trees and ensemble methods, dimension reduction.

## Undergraduate Research

Independent research conducted under the guidance of a faculty member.

## College Algebra

Study of the properties of algebraic, exponential, and logarithmic functions as needed for pre-calculus and calculus.

## Probability Theory

This course is a mathematical introduction to probability theory, covering random variables, moments, multivariate distributions, law of large numbers, central limit theorem, and large deviations.

## Mathematical Problem Solving

Pass/Fail basis. This course is intended to teach general mathematical problem solving skills, and to prepare students to take the Putnam Examination.

## Undergraduate Seminar

Pass/fail basis.

This course provides students with a broad exposure to areas of mathematics research through weekly speakers.

## Probability and Statistics with Applications

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression and analysis of variance.

## A Second Course on Linear Algebra

This course will cover important topics in linear algebra not usually discussed in a first-semester course, featuring a mixture of theory and applications.

## Introduction to Discrete Mathematics

Mathematical logic and proof, mathematical induction, counting methods, recurrence relations, algorithms and complexity, graph theory and graph algorithms.